Blog

Apr 11, 2016 · announcement

New Tools to Summarize Text

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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?

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Please [contact us](mailto:cffl@cloudera.com) 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

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

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Reports

In-depth guides to specific machine learning capabilities
FF24

Text Style Transfer

The NLP task of text style transfer (TST) aims to automatically control the style attributes of a piece of text while preserving the content, which is an important consideration for making NLP more user-centric. In this report, we explore text style transfer through an applied use case — neutralizing subjectivity bias in free text. Along the way, we describe our sequence-to-sequence modeling approach leveraging HuggingFace Transformers, and present a set of custom, reference-free evaluation metrics for quantifying model performance. Finally, we conclude with a discussion of ethics centered around our prototype: Exploring Intelligent Writing Assistance.
Read the report  →
FF22

Inferring Concept Drift Without Labeled Data

Concept drift occurs when the statistical properties of a target domain change overtime causing model performance to degrade. Drift detection is generally achieved by monitoring a performance metric of interest and triggering a retraining pipeline when that metric falls below some designated threshold. However, this approach assumes ample labeled data is available at prediction time - an unrealistic constraint for many production systems. In this report, we explore various approaches for dealing with concept drift when labeled data is not readily accessible.
Read the report  →
FF19

Session-based Recommender Systems

Being able to recommend an item of interest to a user (based on their past preferences) is a highly relevant problem in practice. A key trend over the past few years has been session-based recommendation algorithms that provide recommendations solely based on a user’s interactions in an ongoing session, and which do not require the existence of user profiles or their entire historical preferences. This report explores a simple, yet powerful, NLP-based approach (word2vec) to recommend a next item to a user. While NLP-based approaches are generally employed for linguistic tasks, here we exploit them to learn the structure induced by a user’s behavior or an item’s nature.
Read the report  →
FF18

Few-Shot Text Classification

Text classification can be used for sentiment analysis, topic assignment, document identification, article recommendation, and more. While dozens of techniques now exist for this fundamental task, many of them require massive amounts of labeled data in order to be useful. Collecting annotations for your use case is typically one of the most costly parts of any machine learning application. In this report, we explore how latent text embeddings can be used with few (or even zero) training examples and provide insights into best practices for implementing this method.
Read the report  →

Prototypes

Machine learning prototypes and interactive notebooks
Notebook

ASR with Whisper

Explore the capabilities of OpenAI's Whisper for automatic speech recognition by creating your own voice recordings!
https://colab.research.google.com/github/fastforwardlabs/whisper-openai/blob/master/WhisperDemo.ipynb
Library

NeuralQA

A usable library for question answering on large datasets.
https://neuralqa.fastforwardlabs.com
Notebook

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
Notebooks

NLP for Question Answering

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

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.

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