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.

Read more

May 31, 2018 · newsletter
Apr 25, 2018 · newsletter

Latest posts

Nov 15, 2022 · newsletter

CFFL November Newsletter

November 2022 Perhaps November conjures thoughts of holiday feasts and festivities, but for us, it’s the perfect time to chew the fat about machine learning! Make room on your plate for a peek behind the scenes into our current research on harnessing synthetic image generation to improve classification tasks. And, as usual, we reflect on our favorite reads of the month. New Research! In the first half of this year, we focused on natural language processing with our Text Style Transfer blog series. more
Nov 14, 2022 · post

Implementing CycleGAN

by Michael Gallaspy · Introduction This post documents the first part of a research effort to quantify the impact of synthetic data augmentation in training a deep learning model for detecting manufacturing defects on steel surfaces. We chose to generate synthetic data using CycleGAN,1 an architecture involving several networks that jointly learn a mapping between two image domains from unpaired examples (I’ll elaborate below). Research from recent years has demonstrated improvement on tasks like defect detection2 and image segmentation3 by augmenting real image data sets with synthetic data, since deep learning algorithms require massive amounts of data, and data collection can easily become a bottleneck. more
Oct 20, 2022 · newsletter

CFFL October Newsletter

October 2022 We’ve got another action-packed newsletter for October! Highlights this month include the re-release of a classic CFFL research report, an example-heavy tutorial on Dask for distributed ML, and our picks for the best reads of the month. Open Data Science Conference Cloudera Fast Forward Labs will be at ODSC West near San Fransisco on November 1st-3rd, 2022! If you’ll be in the Bay Area, don’t miss Andrew and Melanie who will be presenting our recent research on Neutralizing Subjectivity Bias with HuggingFace Transformers. more
Sep 21, 2022 · newsletter

CFFL September Newsletter

September 2022 Welcome to the September edition of the Cloudera Fast Forward Labs newsletter. This month we’re talking about ethics and we have all kinds of goodies to share including the final installment of our Text Style Transfer series and a couple of offerings from our newest research engineer. Throw in some choice must-reads and an ASR demo, and you’ve got yourself an action-packed newsletter! New Research! Ethical Considerations When Designing an NLG System In the final post of our blog series on Text Style Transfer, we discuss some ethical considerations when working with natural language generation systems, and describe the design of our prototype application: Exploring Intelligent Writing Assistance. more
Sep 8, 2022 · post

Thought experiment: Human-centric machine learning for comic book creation

by Michael Gallaspy · This post has a companion piece: Ethics Sheet for AI-assisted Comic Book Art Generation I want to make a comic book. Actually, I want to make tools for making comic books. See, the problem is, I can’t draw too good. I mean, I’m working on it. Check out these self portraits drawn 6 months apart: Left: “Sad Face”. February 2022. Right: “Eyyyy”. August 2022. But I have a long way to go until my illustrations would be considered professional quality, notwithstanding the time it would take me to develop the many other skills needed for making comic books. more
Aug 18, 2022 · newsletter

CFFL August Newsletter

August 2022 Welcome to the August edition of the Cloudera Fast Forward Labs newsletter. This month we’re thrilled to introduce a new member of the FFL team, share TWO new applied machine learning prototypes we’ve built, and, as always, offer up some intriguing reads. New Research Engineer! If you’re a regular reader of our newsletter, you likely noticed that we’ve been searching for new research engineers to join the Cloudera Fast Forward Labs team. more

Popular posts

Oct 30, 2019 · newsletter
Exciting Applications of Graph Neural Networks
Nov 14, 2018 · post
Federated learning: distributed machine learning with data locality and privacy
Apr 10, 2018 · post
PyTorch for Recommenders 101
Oct 4, 2017 · post
First Look: Using Three.js for 2D Data Visualization
Aug 22, 2016 · whitepaper
Under the Hood of the Variational Autoencoder (in Prose and Code)
Feb 24, 2016 · post
"Hello world" in Keras (or, Scikit-learn versus Keras)


In-depth guides to specific machine learning capabilities


Machine learning prototypes and interactive notebooks

ASR with Whisper

Explore the capabilities of OpenAI's Whisper for automatic speech recognition by creating your own voice recordings!


A usable library for question answering on large datasets.

Explain BERT for Question Answering Models

Tensorflow 2.0 notebook to explain and visualize a HuggingFace BERT for Question Answering model.

NLP for Question Answering

Ongoing posts and code documenting the process of building a question answering model.

Cloudera Fast Forward Labs

Making the recently possible useful.

Cloudera Fast Forward Labs is an applied machine learning research group. Our mission is to empower enterprise data science practitioners to apply emergent academic research to production machine learning use cases in practical and socially responsible ways, while also driving innovation through the Cloudera ecosystem. Our team brings thoughtful, creative, and diverse perspectives to deeply researched work. In this way, we strive to help organizations make the most of their ML investment as well as educate and inspire the broader machine learning and data science community.

Cloudera   Blog   Twitter

©2022 Cloudera, Inc. All rights reserved.