Apr 11, 2016 · announcement

New Tools to Summarize Text


We’re excited to introduce the latest report and prototype from our machine intelligence R&D group! In this iteration, we explore summarization, or neural network techniques for making unstructured text data computable.

Making language computable has been a goal of computer science research for decades. Historically, it has been a challenge to merely collect and store data. But it’s now so cheap to store data that we often have the opposite problem: once we’ve data, how should we analyze it to find meaning and insights?

Many organizations have made good headway processing structured, transactional data for Business Intelligence, but few have extended analytics to compress insights from the emails, news articles, reports, legal documents, and other troves of written documents that make up the lifeblood of organizations. 

But we’re beginning to gain the ability to do remarkable things with unstructured text. Businesses that adopt this technology will see significant advantages. They will find important information faster. They will expand the horizons of how and what they read, gleaning actionable insights from document corpuses too large for humans to process.

Our work addresses multi- and single-document summarization, illustrating the best technical approaches with two prototypes. Our multi-document prototype uses Latent Dirichlet Allocation to map topics and collect key points of view across thousands of Amazon product reviews. 

Our single-document prototype, Brief, uses skip-thoughts and recurrent neural networks to extract the sentences that best represent the key ideas in a longer document. You can see how Brief scores and highlights an article’s most interesting sentences in our public preview

Our report records lessons we learned building our prototype, teaching readers:

  • how different algorithms represent unstructured text quantitatively
  • why recent breakthroughs in deep learning allow us to model meaning 
  • how to build a summarization system and setbacks to avoid 
  • who the key summarization vendors are and what they offer
  • where natural language processing will go in the near future

We’re excited to help our clients identify opportunities to use these capabilities in their businesses, be that to facilitate research on investments, find documents relevant for a legal matter, manage email overload after vacation, or automatically generate tweets. What else do you imagine?


Please [contact us]( if you’d like to learn more about our research or advising services. 

And join us May 24 for an online discussion with New York NLP startup Agolo, where we’ll explain how summarization technology works and how businesses are using it today! 

Read more

Apr 15, 2016 · post
Apr 6, 2016 · guest post

Latest posts

Nov 15, 2020 · post

Representation Learning 101 for Software Engineers

by Victor Dibia · Figure 1: Overview of representation learning methods. TLDR; Good representations of data (e.g., text, images) are critical for solving many tasks (e.g., search or recommendations). Deep representation learning yields state of the art results when used to create these representations. In this article, we review methods for representation learning and walk through an example using pretrained models. Introduction Deep Neural Networks (DNNs) have become a particularly useful tool in building intelligent systems that simplify cognitive tasks for users. more
Jun 22, 2020 · post

How to Explain HuggingFace BERT for Question Answering NLP Models with TF 2.0

by Victor · Given a question and a passage, the task of Question Answering (QA) focuses on identifying the exact span within the passage that answers the question. Figure 1: In this sample, a BERTbase model gets the answer correct (Achaemenid Persia). Model gradients show that the token “subordinate ..” is impactful in the selection of an answer to the question “Macedonia was under the rule of which country?". This makes sense .. good for BERTbase. more
Jun 16, 2020 · notebook

Evaluating QA: Metrics, Predictions, and the Null Response →

by Melanie · A deep dive into computing QA predictions and when to tell BERT to zip it! In our last post, Building a QA System with BERT on Wikipedia, we used the HuggingFace framework to train BERT on the SQuAD2.0 dataset and built a simple QA system on top of the Wikipedia search engine. This time, we’ll look at how to assess the quality of a BERT-like model for Question Answering.
May 19, 2020 · notebook

Building a QA System with BERT on Wikipedia →

by Melanie · So you’ve decided to build a QA system. You want to start with something simple and general so you plan to make it open domain using Wikipedia as a corpus for answering questions. You want to use the best NLP that your compute resources allow (you’re lucky enough to have access to a GPU) so you’re going to focus on the big, flashy Transformer models that are all the rage these days.
Apr 28, 2020 · notebook

Intro to Automated Question Answering →

by Melanie · Welcome to the first edition of the Cloudera Fast Forward blog on Natural Language Processing for Question Answering! Throughout this series, we’ll build a Question Answering (QA) system with off-the-shelf algorithms and libraries and blog about our process and what we find along the way. We hope to wind up with a beginning-to-end documentary that provides:
Apr 1, 2020 · newsletter

Enterprise Grade ML

by Shioulin · At Cloudera Fast Forward, one of the mechanisms we use to tightly couple machine learning research with application is through application development projects for both internal and external clients. The problems we tackle in these projects are wide ranging and cut across various industries; the end goal is a production system that translates data into business impact. What is Enterprise Grade Machine Learning? Enterprise grade ML, a term mentioned in a paper put forth by Microsoft, refers to ML applications where there is a high level of scrutiny for data handling, model fairness, user privacy, and debuggability. 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


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.

Interpretability Revisited: SHAP and LIME

Explore how to use LIME and SHAP for interpretability.


Cloudera Fast Forward is an applied machine learning reseach group.
Cloudera   Blog   Twitter