Posted by Sandeep Tata, Software Engineer, Google Research

Templatic documents, such as receipts, bills, insurance quotes, and others, are extremely common and critical in a diverse range of business workflows. Currently, processing these documents is largely a manual effort, and automated systems that do exist are based on brittle and error-prone heuristics. Consider a document type like invoices, which can be laid out in thousands of different ways — invoices from different companies, or even different departments within the same company, may have slightly different formatting. However, there is a common understanding of the structured information that an invoice should contain, such as an invoice number, an invoice date, the amount due, the pay-by date, and the list of items for which the invoice was sent. A system that can automatically extract all this data has the potential to dramatically improve the efficiency of many business workflows by avoiding error-prone, manual work.

In “Representation Learning for Information Extraction from Form-like Documents”, accepted to ACL 2020, we present an approach to automatically extract structured data from templatic documents. In contrast to previous work on extraction from plain-text documents, we propose an approach that uses knowledge of target field types to identify candidate fields. These are then scored using a neural network that learns a dense representation of each candidate using the words in its neighborhood. Experiments on two corpora (invoices and receipts) show that we’re able to generalize well to unseen layouts.

Why Is This Hard?
The challenge in this information extraction problem arises because it straddles the natural language processing (NLP) and computer vision worlds. Unlike classic NLP tasks, such documents do not contain “natural language” as might be found in regular sentences and paragraphs, but instead resemble forms. Data is often presented in tables, but in addition many documents have multiple pages, frequently with a varying number of sections, and have a variety of layout and formatting clues to organize the information. An understanding of the two-dimensional layout of text on the page is key to understanding such documents. On the other hand, treating this purely as an image segmentation problem makes it difficult to take advantage of the semantics of the text.

Solution Overview
Our approach to this problem allows developers to train and deploy an extraction system for a given domain (like invoices) using two inputs — a target schema (i.e., a list of fields to extract and their corresponding types) and a small collection of documents labeled with the ground truth for use as a training set. Supported field types include basics, such as dates, integers, alphanumeric codes, currency amounts, phone-numbers, and URLs. We also take advantage of entity types commonly detected by the Google Knowledge Graph, such as addresses, names of companies, etc.

The input document is first run through an Optical Character Recognition (OCR) service to extract the text and layout information, which allows this to work with native digital documents, such as PDFs, and document images (e.g., scanned documents). We then run a candidate generator that identifies spans of text in the OCR output that might correspond to an instance of a given field. The candidate generator utilizes pre-existing libraries associated with each field type (date, number, phone-number, etc.), which avoids the need to write new code for each candidate generator. Each of these candidates is then scored using a trained neural network (the “scorer”, described below) to estimate the likelihood that it is indeed a value one might extract for that field. Finally, an assigner module matches the scored candidates to the target fields. By default, the assigner simply chooses the highest scoring candidate for the field, but additional domain-specific constraints can be incorporated, such as requiring that the invoice date field is chronologically before the payment date field.

The processing steps in the extraction system using a toy schema with two fields on an input invoice document. Blue boxes show the candidates for the invoice_date field and gold boxes for the amount_due field.

Scorer
The scorer is a neural model that is trained as a binary classifier. It takes as input the target field from the schema along with the extraction candidate and produces a prediction score between 0 and 1. The target label for a candidate is determined by whether the candidate matches the ground truth for that document and field. The model learns how to represent each field and each candidate in a vector space in which the nearer a field and candidate are in the vector space, the more likely it is that the candidate is the true extraction value for that field and document.

Candidate Representation
A candidate is represented by the tokens in its neighborhood along with the relative position of the token on the page with respect to the centroid of the bounding box identified for the candidate. Using the invoice_date field as an example, phrases in the neighborhood like “Invoice Date’” or “Inv Date” might indicate to the scorer that this is a likely candidate, while phrases like “Delivery Date” would indicate that this is likely not the invoice_date. We do not include the value of the candidate in its representation in order to avoid overfitting to values that happen to be present in a small training data set — e.g., “2019” for the invoice date, if the training corpus happened to include only invoices from that year.

A small snippet of an invoice. The green box shows a candidate for the invoice_date field, and the red box is a token in the neighborhood along with the arrow representing the relative position. Each of the other tokens (‘number’, ‘date’, ‘page’, ‘of’, etc along with the other occurrences of ‘invoice’) are part of the neighborhood for the invoice candidate.

Model Architecture
The figure below shows the general structure of the network. In order to construct the candidate encoding (i), each token in the neighborhood is embedded using a word embedding table (a). The relative position of each neighbor (b) is embedded using two fully connected ReLU layers that capture fine-grained non-linearities. The text and position embeddings for each neighbor are concatenated to form a neighbor encoding (d). A self attention mechanism is used to incorporate the neighborhood context for each neighbor (e), which is combined into a neighborhood encoding (f) using max-pooling. The absolute position of the candidate on the page (g) is embedded in a manner similar to the positional embedding for a neighbor, and concatenated with the neighborhood encoding for the candidate encoding (i). The final scoring layer computes the cosine similarity between the field embedding (k) and the candidate encoding (i) and then rescales it to be between 0 and 1.

Results
For training and validation, we used an internal dataset of invoices with a large variety of layouts. In order to test the ability of the model to generalize to unseen layouts, we used a test-set of invoices with layouts that were disjoint from the training and validation set. We report the F1 score of the extractions from this system on a few key fields below (higher is better):

As you can see from the table above, the model does well on most fields. However, there’s room for improvement for fields like delivery_date. Additional investigation revealed that this field was present in a very small subset of the examples in our training data. We expect that gathering additional training data will help us improve on it.

What’s next?
Google Cloud recently announced an invoice parsing service as part of the Document AI product. The service uses the methods described above, along with other recent research breakthroughs like BERT, to extract more than a dozen key fields from invoices. You can upload an invoice at the demo page and see this technology in action!

For a given document type we expect to be able to build an extraction system given a modest sized labeled corpus. There are several follow-ons we are currently pursuing, including the improvement of data efficiency and accurately handling nested and repeated fields, and fields for which it is difficult to define a good candidate generator.

Acknowledgements
This work was a collaboration between Google Research and several engineers in Google Cloud. I’d like to thank Navneet Potti, James Wendt, Marc Najork, Qi Zhao, and Ivan Kuznetsov in Google Research as well as Lauro Costa, Evan Huang, Will Lu, Lukas Rutishauser, Mu Wang, and Yang Xu on the Cloud AI team for their support. And finally, our research interns Bodhisattwa Majumder and Beliz Gunel for their tireless experimentation on dozens of ideas.

Posted by Patrick Riley, Principal Engineer, Accelerated Science Team, Google Research

Much of the development of therapeutics for human disease is built around understanding and modulating the function of proteins, which are the main workhorses of many biological activities. Small molecule drugs such as ibuprofen often work by inhibiting or promoting the function of proteins or their interactions with other biomolecules. Developing useful “virtual screening” methods where potential small molecules can be evaluated computationally rather than in a lab, has long been an area of research. However, the persistent challenge is to build a method that works well enough across a wide range of chemical space to be useful for finding small molecules with physically verified useful interaction with a protein of interest, i.e., “hits”.

In “Machine learning on DNA-encoded libraries: A new paradigm for hit-finding”, recently published in the Journal of Medicinal Chemistry, we worked in collaboration with X-Chem Pharmaceuticals to demonstrate an effective new method for finding biologically active molecules using a combination of physical screening with DNA-encoded small molecule libraries and virtual screening using a graph convolutional neural network (GCNN). This research has led to the creation of the Chemome initiative, a cooperative project between our Accelerated Science team and ZebiAI that will enable the discovery of many more small molecule chemical probes for biological research.

Background on Chemical Probes
Making sense of the biological networks that support life and produce disease is an immensely complex task. One approach to study these processes is using chemical probes, small molecules that aren’t necessarily useful as drugs, but that selectively inhibit or promote the function of specific proteins. When you have a biological system to study (such as cancer cells growing in a dish), you can add the chemical probe at a specific time and observe how the biological system responds differently when the targeted protein has increased or decreased activity. But, despite how useful chemical probes are for this kind of basic biomedical research, only 4% of human proteins have a known chemical probe available.

The process of finding chemical probes begins similarly to the earliest stages of small molecule drug discovery. Given a protein target of interest, the space of small molecules is scanned to find “hit” molecules that can be further tested. Robotic assisted high throughput screening where up to hundred of thousands or millions of molecules are physically tested is a cornerstone of modern drug research. However, the number of small molecules you can easily purchase (1.2×10⁹) is much larger than that, which in turn is much smaller than the number of small drug like molecules (estimates from 10²⁰ to 10⁶⁰). “Virtual screening” could possibly quickly and efficiently search this vast space of potentially synthesizable molecules and greatly speed up the discovery of therapeutic compounds.

DNA-Encoded Small Molecule Library Screening
The physical part of the screening process uses DNA-encoded small molecule libraries (DELs), which contain many distinct small molecules in one pool, each of which is attached to a fragment of DNA serving as a unique barcode for that molecule. While this basic technique has been around for several decades, the quality of the library and screening process is key to producing meaningful results.

DELs are a very clever idea to solve a biochemical challenge, which is how to collect small molecules into one place with an easy way to identify each. The key is to use DNA as a barcode to identify each molecule, similar to Nobel Prize winning phage display technology. First, one generates many chemical fragments, each with a unique DNA barcode attached, along with a common chemical handle (the NH₂ in this case). The results are then pooled and split into separate reactions where a set of distinct chemical fragments with another common chemical handle (e.g., OH) are added. The chemical fragments from the two steps react and fuse together at the common chemical handles. The DNA fragments are also connected to build one continuous barcode for each molecule. The net result is that by performing 2N operations, one gets N² unique molecules, each of which is identified by its own unique DNA barcode. By using more fragments or more cycles, it’s relatively easy to make libraries with millions or even billions of distinct molecules.

An overview of the process of creating a DNA encoded small molecule library. First, DNA “barcodes” (represented here with numbered helices) are attached to small chemical fragments (the blue shapes) which expose a common chemical “handle” (e.g. the NH2 shown here). When mixed with other chemical fragments (the orange shapes) each of which has another exposed chemical “handle” (the OH) with attached DNA fragments, reactions merge the sets of chemical and DNA fragments, resulting in a voluminous library of small molecules of interest, each with a unique DNA “barcode”.

Once the library has been generated, it can be used to find the small molecules that bind to the protein of interest by mixing the DEL together with the protein and washing away the small molecules that do not attach. Sequencing the remaining DNA barcodes produces millions of individual reads of DNA fragments, which can then be carefully processed to estimate which of the billions of molecules in the original DEL interact with the protein.

Machine Learning on DEL Data
Given the physical screening data returned for a particular protein, we build an ML model to predict whether an arbitrarily chosen small molecule will bind to that protein. The physical screening with the DEL provides positive and negative examples for an ML classifier. To simplify slightly, the small molecules that remain at the end of the screening process are positive examples and everything else are negative examples. We use a graph convolutional neural network, which is a type of neural network specially designed for small graph-like inputs, such as the small molecules in which we are interested.

Results
We physically screened three diverse proteins using DEL libraries: sEH (a hydrolase), ERα (a nuclear receptor), and c-KIT (a kinase). Using the DEL-trained models, we virtually screened large make-on-demand libraries from Mcule and an internal molecule library at X-Chem to identify a diverse set of molecules predicted to show affinity with each target. We compared the results of the GCNN models to a random forest (RF) model, a common method for virtual screening that uses standard chemical fingerprints, which we use as baseline. We find that the GCNN model significantly outperforms the RF model in discovering more potent candidates.

Fraction of molecules (“hit rates”) from those tested showing various levels of activity, comparing predictions from two different machine learned models (a GCNN and random forests, RF) on three distinct protein targets. The color scale on the right uses a common metric IC50 for representing the potency of a molecule. nM means “nanomolar” and µM means “micromolar”. Smaller values / darker colors are generally better molecules. Note that typical virtual screening approaches not built with DEL data normally only reach a few percent on this scale.

Importantly, unlike many other uses of virtual screening, the process to select the molecules to test was automated or easily automatable given the results of the model, and we did not rely on review and selection of the most promising molecules by a trained chemist. In addition, we tested almost 2000 molecules across the three targets, the largest published prospective study of virtual screening of which we are aware. While providing high confidence on the hit rates above, this also allows one to carefully examine the diversity of hits and the usefulness of the model for molecules near and far from the training set.

The Chemome Initiative
ZebiAI Therapeutics was founded based on the results of this research and has partnered with our team and X-Chem Pharmaceuticals to apply these techniques to efficiently deliver new chemical probes to the research community for human proteins of interest, an effort called the Chemome Initiative.

As part of the Chemome Initiative, ZebiAI will work with researchers to identify proteins of interest and source screening data, which our team will use to build machine learning models and make predictions on commercially available libraries of small molecules. ZebiAI will provide the predicted molecules to researchers for activity testing and will collaborate with researchers to advance some programs through discovery. Participation in the program requires that the validated hits be published within a reasonable time frame so that the whole community can benefit. While more validation must be done to make the hit molecules useful as chemical probes, especially for specifically targeting the protein of interest and the ability to function correctly in common assays, having potent hits is a big step forward in the process.

We’re excited to be a part of the Chemome Initiative enabled by the effective ML techniques described here and look forward to its discovery of many new chemical probes. We expect the Chemome will spur significant new biological discoveries and ultimately accelerate new therapeutic discovery for the world.

Acknowledgements
This work represents a multi-year effort between the Accelerated Science Team and X-Chem Pharmaceuticals with many people involved. This project would not have worked without the combined diverse skills of biologists, chemists, and ML researchers. We should especially acknowledge Eric Sigel (of X-Chem, now at ZebiAI) and Kevin McCloskey (of Google), the first authors on the paper and Steve Kearnes (of Google) for core modelling ideas and technical work.

Posted by Peter J. Liu and Yao Zhao, Software Engineers, Google Research

Students are often tasked with reading a document and producing a summary (for example, a book report) to demonstrate both reading comprehension and writing ability. This abstractive text summarization is one of the most challenging tasks in natural language processing, involving understanding of long passages, information compression, and language generation. The dominant paradigm for training machine learning models to do this is sequence-to-sequence (seq2seq) learning, where a neural network learns to map input sequences to output sequences. While these seq2seq models were initially developed using recurrent neural networks, Transformer encoder-decoder models have recently become favored as they are more effective at modeling the dependencies present in the long sequences encountered in summarization.

Transformer models combined with self-supervised pre-training (e.g., BERT, GPT-2, RoBERTa, XLNet, ALBERT, T5, ELECTRA) have shown to be a powerful framework for producing general language learning, achieving state-of-the-art performance when fine-tuned on a wide array of language tasks. In prior work, the self-supervised objectives used in pre-training have been somewhat agnostic to the down-stream application in favor of generality; we wondered whether better performance could be achieved if the self-supervised objective more closely mirrored the final task.

In “PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization” (to appear at the 2020 International Conference on Machine Learning), we designed a pre-training self-supervised objective (called gap-sentence generation) for Transformer encoder-decoder models to improve fine-tuning performance on abstractive summarization, achieving state-of-the-art results on 12 diverse summarization datasets. Supplementary to the paper, we are also releasing the training code and model checkpoints on GitHub.

A Self-Supervised Objective for Summarization
Our hypothesis is that the closer the pre-training self-supervised objective is to the final down-stream task, the better the fine-tuning performance. In PEGASUS pre-training, several whole sentences are removed from documents and the model is tasked with recovering them. An example input for pre-training is a document with missing sentences, while the output consists of the missing sentences concatenated together. This is an incredibly difficult task that may seem impossible, even for people, and we don’t expect the model to solve it perfectly. However, such a challenging task encourages the model to learn about language and general facts about the world, as well as how to distill information taken from throughout a document in order to generate output that closely resembles the fine-tuning summarization task. The advantage of this self-supervision is that you can create as many examples as there are documents, without any human annotation, which is often the bottleneck in purely supervised systems.

A self-supervised example for PEGASUS during pre-training. The model is trained to output all the masked sentences.

We found that choosing “important” sentences to mask worked best, making the output of self-supervised examples even more similar to a summary. We automatically identified these sentences by finding those that were most similar to the rest of the document according to a metric called ROUGE. ROUGE computes the similarity of two texts by computing n-gram overlaps using a score from 0 to 100 (ROUGE-1, ROUGE-2, and ROUGE-L are three common variants).

Similar to other recent methods, such as T5, we pre-trained our model on a very large corpus of web-crawled documents, then we fine-tuned the model on 12 public down-stream abstractive summarization datasets, resulting in new state-of-the-art results as measured by automatic metrics, while using only 5% of the number of parameters of T5. The datasets were chosen to be diverse, including news articles, scientific papers, patents, short stories, e-mails, legal documents, and how-to directions, showing that the model framework is adaptive to a wide-variety of topics.

Fine-Tuning with Small Numbers of Examples
While PEGASUS showed remarkable performance with large datasets, we were surprised to learn that the model didn’t require a large number of examples for fine-tuning to get near state-of-the-art performance:

ROUGE scores (three variants, higher is better) vs. the number of supervised examples across four selected summarization datasets. The dotted-line shows the Transformer encoder-decoder performance with full-supervision, but without pre-training.

With only 1000 fine-tuning examples, we were able to perform better in most tasks than a strong baseline (Transformer encoder-decoder) that used the full supervised data, which in some cases had many orders of magnitude more examples. This “sample efficiency” greatly increases the usefulness of text summarization models as it significantly lowers the scale and cost of supervised data collection, which in the case of summarization is very expensive.

Human-Quality summaries
While we find automatic metrics such as ROUGE are useful proxies for measuring progress during model development, they only provide limited information and don’t tell us the whole story, such as fluency or a comparison to human performance. To this end, we conducted a human evaluation, where raters were asked to compare summaries from our model with human ones (without knowing which is which). This has some similarities to the Turing test.

Human raters were asked to rate model and human-written summaries without knowing which was which. The document is truncated here for illustration, but raters see the full text.

We performed the experiment with 3 different datasets and found that human raters do not consistently prefer the human summaries to those from our model. Furthermore, our models trained with only 1000 examples performed nearly as well. In particular, with the much studied XSum and CNN/Dailymail datasets, the model achieves human-like performance using only 1000 examples. This suggests large datasets of supervised examples are no longer necessary for summarization, opening up many low-cost use-cases.

A Test of Comprehension: Counting Ships
Following this post is an example article from the XSum dataset along with the model-generated abstractive summary. The model correctly abstracts and paraphrases four named frigates (HMS Cumberland, HMS Campbeltown, HMS Chatham and HMS Cornwall) as “four Royal Navy frigates”, something an extractive approach could not do since “four” is not mentioned anywhere. Was this a fluke or did the model actually count? One way to find out is to add and remove ships to see if the count changes.

As can be seen below, the model successfully “counts” ships from 2 to 5. However, when we add a sixth ship, the “HMS Alphabet”, it miscounts it as “seven”. So it appears the model has learned to count small numbers of items in a list, but does not yet generalize as elegantly as we would hope. Still, we think this rudimentary counting ability is impressive as it was not explicitly programmed into the model, and it demonstrates a limited amount of “symbolic reasoning” by the model.

PEGASUS code and model release
To support on-going research in this field and ensure reproducibility, we are releasing the PEGASUS code and model checkpoints on GitHub. This includes fine-tuning code which can be used to adapt PEGASUS to other summarization datasets.

Acknowledgements
This work has been a collaborative effort involving Jingqing Zhang, Yao Zhao, Mohammad Saleh, and Peter J. Liu. We thank the T5 and Google News teams for providing datasets for pre-training PEGASUS.

• Four ships
  The decommissioned Type 22 frigates
  HMS Cumberland, HMS Campbeltown, HMS Chatham and HMS Cornwall
  are currently moored in Portsmouth Harbour.
  Bidders had until 23 January to register an interest in the former Devonport-based ships. The BBC understands no proposals to preserve the ships have been submitted. Those who have registered an interest are finalising their bids with viewings set to take place in late February and March. A final decision is not expected until the spring. The government’s Disposal Services Authority, which is handling the sale, wants to award at least one of the frigates to a UK ship recycler to determine the capacity of the UK’s industry in the field. Penny Mordaunt, Conservative MP for Portsmouth North, said it was important UK recyclers had the chance to prove themselves in the field but she was also keen to see at least one of them saved from the scrapyard. She added: “For anyone that has served on a ship it’s your home, you’ve literally been through the wars with it… and you want them to have a noble second life. “My preference is to go for the reef and diving attraction. “We’ve got to get best value for the budget but a reef would also generate income for part of the country through tourism.” The Ministry of Defence has previously said it will “consider all options” for the frigates to ensure “best financial return for the taxpayer”. A spokeswoman would not comment on the number or nature of the bids received due to “commercial sensitivity”. Originally designed as a specialist anti-submarine ship, the Type 22 frigate evolved into a powerful surface combatant with substantial anti-surface, anti-submarine and anti-aircraft weapons systems. They were also known for having excellent command and control, and communication facilities, making them ideal flagships on deployments, with a complement of about 280 crew. Last year, the aircraft carrier HMS Ark Royal was sold as scrap for £3m.
  

  ---

  Model Summary: No proposals have been submitted to preserve four Royal Navy frigates for reuse, the BBC has learned.

• Five ships
  The decommissioned Type 22 frigates
  HMS Cumberland, HMS Campbeltown, HMS Chatham, HMS Google and HMS Cornwall
  are currently moored in Portsmouth Harbour.
  Bidders had until 23 January to register an interest in the former Devonport-based ships. The BBC understands no proposals to preserve the ships have been submitted. Those who have registered an interest are finalising their bids with viewings set to take place in late February and March. A final decision is not expected until the spring. The government’s Disposal Services Authority, which is handling the sale, wants to award at least one of the frigates to a UK ship recycler to determine the capacity of the UK’s industry in the field. Penny Mordaunt, Conservative MP for Portsmouth North, said it was important UK recyclers had the chance to prove themselves in the field but she was also keen to see at least one of them saved from the scrapyard. She added: “For anyone that has served on a ship it’s your home, you’ve literally been through the wars with it… and you want them to have a noble second life. “My preference is to go for the reef and diving attraction. “We’ve got to get best value for the budget but a reef would also generate income for part of the country through tourism.” The Ministry of Defence has previously said it will “consider all options” for the frigates to ensure “best financial return for the taxpayer”. A spokeswoman would not comment on the number or nature of the bids received due to “commercial sensitivity”. Originally designed as a specialist anti-submarine ship, the Type 22 frigate evolved into a powerful surface combatant with substantial anti-surface, anti-submarine and anti-aircraft weapons systems. They were also known for having excellent command and control, and communication facilities, making them ideal flagships on deployments, with a complement of about 280 crew. Last year, the aircraft carrier HMS Ark Royal was sold as scrap for £3m.
  

  ---

  Model Summary: No bids have been submitted for the sale of five Royal Navy frigates, the BBC understands.

• Two ships
  The decommissioned Type 22 frigates
  HMS Google and HMS Alphabet
  are currently moored in Portsmouth Harbour.
  Bidders had until 23 January to register an interest in the former Devonport-based ships. The BBC understands no proposals to preserve the ships have been submitted. Those who have registered an interest are finalising their bids with viewings set to take place in late February and March. A final decision is not expected until the spring. The government’s Disposal Services Authority, which is handling the sale, wants to award at least one of the frigates to a UK ship recycler to determine the capacity of the UK’s industry in the field. Penny Mordaunt, Conservative MP for Portsmouth North, said it was important UK recyclers had the chance to prove themselves in the field but she was also keen to see at least one of them saved from the scrapyard. She added: “For anyone that has served on a ship it’s your home, you’ve literally been through the wars with it… and you want them to have a noble second life. “My preference is to go for the reef and diving attraction. “We’ve got to get best value for the budget but a reef would also generate income for part of the country through tourism.” The Ministry of Defence has previously said it will “consider all options” for the frigates to ensure “best financial return for the taxpayer”. A spokeswoman would not comment on the number or nature of the bids received due to “commercial sensitivity”. Originally designed as a specialist anti-submarine ship, the Type 22 frigate evolved into a powerful surface combatant with substantial anti-surface, anti-submarine and anti-aircraft weapons systems. They were also known for having excellent command and control, and communication facilities, making them ideal flagships on deployments, with a complement of about 280 crew. Last year, the aircraft carrier HMS Ark Royal was sold as scrap for £3m.
  

  ---

  Model Summary: Two Royal Navy frigates set to be sold as scrap are unlikely to be preserved, the BBC understands.

• Three ships
  The decommissioned Type 22 frigates
  HMS Cumberland, HMS Campbeltown and HMS Cornwall
  are currently moored in Portsmouth Harbour.
  Bidders had until 23 January to register an interest in the former Devonport-based ships. The BBC understands no proposals to preserve the ships have been submitted. Those who have registered an interest are finalising their bids with viewings set to take place in late February and March. A final decision is not expected until the spring. The government’s Disposal Services Authority, which is handling the sale, wants to award at least one of the frigates to a UK ship recycler to determine the capacity of the UK’s industry in the field. Penny Mordaunt, Conservative MP for Portsmouth North, said it was important UK recyclers had the chance to prove themselves in the field but she was also keen to see at least one of them saved from the scrapyard. She added: “For anyone that has served on a ship it’s your home, you’ve literally been through the wars with it… and you want them to have a noble second life. “My preference is to go for the reef and diving attraction. “We’ve got to get best value for the budget but a reef would also generate income for part of the country through tourism.” The Ministry of Defence has previously said it will “consider all options” for the frigates to ensure “best financial return for the taxpayer”. A spokeswoman would not comment on the number or nature of the bids received due to “commercial sensitivity”. Originally designed as a specialist anti-submarine ship, the Type 22 frigate evolved into a powerful surface combatant with substantial anti-surface, anti-submarine and anti-aircraft weapons systems. They were also known for having excellent command and control, and communication facilities, making them ideal flagships on deployments, with a complement of about 280 crew. Last year, the aircraft carrier HMS Ark Royal was sold as scrap for £3m.
  

  ---

  Model Summary: No proposals have been submitted to preserve three Royal Navy frigates for reuse, the BBC has learned.

• Six ships
  The decommissioned Type 22 frigates
  HMS Cumberland, HMS Campbeltown, HMS Chatham, HMS Google, HMS Alphabet and HMS Cornwall
  are currently moored in Portsmouth Harbour.
  Bidders had until 23 January to register an interest in the former Devonport-based ships. The BBC understands no proposals to preserve the ships have been submitted. Those who have registered an interest are finalising their bids with viewings set to take place in late February and March. A final decision is not expected until the spring. The government’s Disposal Services Authority, which is handling the sale, wants to award at least one of the frigates to a UK ship recycler to determine the capacity of the UK’s industry in the field. Penny Mordaunt, Conservative MP for Portsmouth North, said it was important UK recyclers had the chance to prove themselves in the field but she was also keen to see at least one of them saved from the scrapyard. She added: “For anyone that has served on a ship it’s your home, you’ve literally been through the wars with it… and you want them to have a noble second life. “My preference is to go for the reef and diving attraction. “We’ve got to get best value for the budget but a reef would also generate income for part of the country through tourism.” The Ministry of Defence has previously said it will “consider all options” for the frigates to ensure “best financial return for the taxpayer”. A spokeswoman would not comment on the number or nature of the bids received due to “commercial sensitivity”. Originally designed as a specialist anti-submarine ship, the Type 22 frigate evolved into a powerful surface combatant with substantial anti-surface, anti-submarine and anti-aircraft weapons systems. They were also known for having excellent command and control, and communication facilities, making them ideal flagships on deployments, with a complement of about 280 crew. Last year, the aircraft carrier HMS Ark Royal was sold as scrap for £3m.
  

  ---

  Model Summary: Seven Royal Navy frigates are set to be put up for sale.