Blog

Apr 21, 2016 · post

Summarization as a Gateway to Computable Language

Analyzing unstructured text data such as news, emails, chats, narrative prose, legal documents, or transcribed speech is an extremely tough problem. Thanks to massive leaps in data engineering, we can just about store and retrieve this torrent of information. But we can’t yet conduct the kind of rich and fast analyses that we take for granted with structured, quantitative data.

Our newly released summarization report is a response to this problem in two senses.

Summaries make documents more manageable

The first is the more obvious: by definition, summarization makes documents shorter and more manageable while retaining meaning. If you want to learn how to build automatically generated extractive summaries in your product, then the specific algorithms and prototypes we describe will definitely be interesting.

Summarization algorithms therefore have embedded within them a key component of one of the most fundamental problems in machine intelligence: how to extract and process the meaning of human language.

But the second way summarization is relevant to the problem of analyzing unstructured text is more general and, we think, more significant.

To automatically summarize text, a necessary first step is to vectorize it. That is, to rewrite it as a sequence of numbers that a computer can operate on. There are lots of ways to do this. The best (such as topic models or neural-network-based language embeddings such as skip-thoughts) are more than just counts of words. They do a good job of retaining the semantic meaning of the document in a way that is accessible to computers. We talk about them more in the report.

Done well, vectorization allows the subsequent steps of a summarization algorithm to find the key ideas in a document, which it can use to generate a summary. But vectorization is also the first step for the countless other tasks that, like summarization, implicitly involve the computer working with the meaning of a document.

The technologies we use to summarize documents have many other potential uses

We’re really excited about the summarization algorithms and prototypes we describe in the report, which are great solutions to a valuable specific task. But we’re perhaps even more excited about the way in which they point to better approaches to simplification, translation, semantic search, document clustering, image caption generation, and even speech recognition.  In that sense, they open a gateway to a future in which machine intelligence can truly understand human language.

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Nov 15, 2022 · newsletter

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Sep 8, 2022 · post

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

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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
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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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https://qa.fastforwardlabs.com

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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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