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May 2, 2018 · newsletter

Progress in text summarization

We published our report on text summarization in 2016. Since then, we’ve enjoyed helping our clients make use of techniques such as topic modeling, document embedding, and recurrent neural networks to deal with text that ranges in scope from product reviews to insurance documents to call transcripts to news.

Our goal when we do research is to address capabilities and technologies that we expect to become production-ready in one to two years. That focus on fast-moving areas means that new algorithmic ideas sometimes come along that allow our clients to extend or improve upon the work in our reports. Prompted in part by Yue Dong’s March 2018 Survey on Neural Network-Based Summarization Methods, we thought we’d take some time to describe the developments in text summarization since our report was published.

Put simply: there’s bad news and there’s good news.

The bad news is that we still don’t think abstractive summarization is ready for production prime time. Extractive summarization involves selecting a few passages from a document or corpus and stitching them together to form a summary. As we discuss in our report, this is a tough problem. But abstractive summarization is harder still. Not only must you identify the salient ideas, but you must also generate new text that expresses those ideas concisely.

Pointer network for summarization. Credit: Abigail See and collaborators

There has been considerable progress along these lines. The current state of the art is 2017’s attention-based pointer networks, e.g. work from Abigail See and collaborators at Stanford and Google Brain and Salesforce Research’s work lead by Romain Paulus.

However, those authors would concede what Noah Weber and collaborators showed last month: in practice, these abstractive networks work by “mostly, if not entirely, copying over phrases, sentences, and sometimes multiple consecutive sentences from an input paragraph, effectively performing extractive summarization.” So, for now at least, you get the training data requirements and engineering complexity of cutting-edge deep learning without the practical performance increase. Which is not to say academic work on abstractive summarization is at a dead end; we look forward to reporting on the inevitable breakthroughs in a year or two.

In the meantime, we promised good news!

Extractive summarization with reinforcement learning. Credit: Shashi Narayan and collaborators

The good news is a couple of really nice papers that make concrete improvements to extractive summarization. Both are from the same Edinburgh group. Neural Extractive Summarization with Side Information (2017) takes advantage of a very natural heuristic that was used in classical summarization algorithms: titles and image captions are particularly strong signals of the important ideas in a document. This heuristic is incorporated into an attention-based encoder-decoder network, and they get really nice extractive results. If your source documents have that kind of structure, this approach is worth investigating. More ambitiously, in Ranking Sentences for Extractive Summarization with Reinforcement Learning (2018) the same group recasts extractive summarization as a reinforcement learning task. Unusually, they learn to rank sentences in the source document rather than score them in isolation, which they argue results in more coherent (and less verbose) overall summaries.

So, two years after our report, text summarization remains not only a useful business capability, but a very vibrant area of research.

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Latest posts

Sep 22, 2021 · post

Automatic Summarization from TextRank to Transformers

by Melanie Beck · Automatic summarization is a task in which a machine distills a large amount of data into a subset (the summary) that retains the most relevant and important information from the whole. While traditionally applied to text, automatic summarization can include other formats such as images or audio. In this article we’ll cover the main approaches to automatic text summarization, talk about what makes for a good summary, and introduce Summarize. – a summarization prototype we built that showcases several automatic summarization techniques.
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Sep 21, 2021 · post

Extractive Summarization with Sentence-BERT

by Victor Dibia · In extractive summarization, the task is to identify a subset of text (e.g., sentences) from a document that can then be assembled into a summary. Overall, we can treat extractive summarization as a recommendation problem. That is, given a query, recommend a set of sentences that are relevant. The query here is the document, relevance is a measure of whether a given sentence belongs in the document summary. How we go about obtaining this measure of relevance varies (a common dilemma for any recommendation system).
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Sep 20, 2021 · post

How (and when) to enable early stopping for Gensim's Word2Vec

by Melanie Beck · The Gensim library is a staple of the NLP stack. While it primarily focuses on topic modeling and similarity for documents, it also supports several word embedding algorithms, including what is likely the best-known implementation of Word2Vec. Word embedding models like Word2Vec use unlabeled data to learn vector representations for each token in a corpus. These embeddings can then be used as features in myriad downstream tasks such as classification, clustering, or recommendation systems.
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Jul 7, 2021 · post

Exploring Multi-Objective Hyperparameter Optimization

By Chris and Melanie. The machine learning life cycle is more than data + model = API. We know there is a wealth of subtlety and finesse involved in data cleaning and feature engineering. In the same vein, there is more to model-building than feeding data in and reading off a prediction. ML model building requires thoughtfulness both in terms of which metric to optimize for a given problem, and how best to optimize your model for that metric!
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Jun 9, 2021 ·

Deep Metric Learning for Signature Verification

By Victor and Andrew. TLDR; This post provides an overview of metric learning loss functions (constrastive, triplet, quadruplet, and group loss), and results from applying contrastive and triplet loss to the task of signature verification. A complete list of the posts in this series is outlined below: Pretrained Models as Baselines for Signature Verification -- Part 1: Deep Learning for Automatic Offline Signature Verification: An Introduction Part 2: Pretrained Models as Baselines for Signature Verification Part 3: Deep Metric Learning for Signature Verification In our previous blog post, we discussed how pretrained models can serve as strong baselines for the task of signature verification.
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May 27, 2021 · post

Pretrained Models as a Strong Baseline for Automatic Signature Verification

By Victor and Andrew. Figure 1. Baseline approach for automatic signature verification using pretrained models TLDR; This post describes how pretrained image classification models can be used as strong baselines for the task of signature verification. The full list of posts in the series is outlined below: Pretrained Models as Baselines for Signature Verification -- Part 1: Deep Learning for Automatic Offline Signature Verification: An Introduction Part 2: Pretrained Models as Baselines for Signature Verification Part 3: Deep Metric Learning for Signature Verification As discussed in our introductory blog post, offline signature verification is a biometric verification task that aims to discriminate between genuine and forged samples of handwritten signatures.
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NeuralQA

A usable library for question answering on large datasets.
https://neuralqa.fastforwardlabs.com
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Explain BERT for Question Answering Models

Tensorflow 2.0 notebook to explain and visualize a HuggingFace BERT for Question Answering model.
https://colab.research.google.com/drive/1tTiOgJ7xvy3sjfiFC9OozbjAX1ho8WN9?usp=sharing
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NLP for Question Answering

Ongoing posts and code documenting the process of building a question answering model.
https://qa.fastforwardlabs.com
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Interpretability Revisited: SHAP and LIME

Explore how to use LIME and SHAP for interpretability.
https://colab.research.google.com/drive/1pjPzsw_uZew-Zcz646JTkRDhF2GkPk0N

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