Blog

Mar 25, 2016 · post

H.P. Luhn and the Heuristic Value of Simplicity

image

The Fast Forward Labs team is putting final touches on our Summarization research, which explains approaches to making text quantifiable and computable. Stay tuned for a series of resources on the topic, including an online talk May 24 where we’ll cover technical details and survey use cases for financial services, media, and professional services with Agolo. Sign up here!

In writing our reports, we try not only to inform readers about the libraries, math, and techniques they can use to put a system into production today, but also the lessons they can learn from historical approaches to a given topic. Turning a retrospective eye towards past work can be particularly helpful when using an algorithm like a recurrent neural network. That’s because neural networks are notoriously hard to interpret: feature engineering is left to the algorithm, and involves a complex interplay between the weight of individual computing nodes and the connections that link them together. In the face of this complexity, it can be helpful to keep former, more simple techniques in mind as a heuristic guide - or model - to shape our intuition about how contemporary algorithms work.

For example, we found that H.P. Luhn’s 1958 paper The Automatic Creation of Literary Abstracts provided a simple heuristic to help wrap our heads around the basic steps that go into probabilistic models for summarizing text. (For those interested in history, Luhn also wrote a paper about business intelligence in 1958 that feels like it could have been written today, as it highlights the growing need to automate information retrieval to manage an unwieldy amount of information.) Our design lead, Grant Custer, designed a prototype you can play with to walk through Luhn’s method. 

Here’s the link to access the live demo. Feel free to use the suggested text, or to play around your own (and share results on Twitter!). 

Luhn’s algorithm begins by transforming the content of sentences into a mathematical expression, or vector. He uses a “bag of words” model, which ignores filler words like “the” or “and”, and counts the number of times remaining words appear in each sentence.

image

Luhn’s intuition was that a word that appears many times in a document is important to the document’s meaning. Under this assumption, a sentence that contains many of the words that appear many times in the overall document is itself highly representative of that document. In our demo, if a document’s most significant words are protein (appears 8 times) and DNA (appears 7 times), then this implies that the sentence “Proteins are made by tiny machines in the cell called ribosomes” is a useful one to extract in the summary. Once this sentence scoring is complete, the last step is simply to select those sentences with the highest overall rankings. 

image

Luhn himself notes that his method for determining the relative significance of sentences “gives no consideration to the meaning of words or the arguments expressed by word combinations.” It is, rather, a “probabilistic one” based on counting. The algorithm does not select the sentence about ribosomes given its understanding of the importance ribosomes have to the topic in question; it selects that sentence because it densely packs together words that appear frequently across the longer document. 

The contemporary approaches we study in our upcoming report build upon this basic theme. One approach, topic modeling using Latent Dirichlet Allocation (LDA), groups together words that co-occur into mathematical expressions called topics and then represents documents as a short vector of different topics weights (e.g., 50% words from the the protein topic, 30% words from the DNA topic, and 20% from the gene topic). While the mathematical models that determine these topics are much more complex than simply counting a bag of words, LDA borrows Luhn’s basic insight: that we can quantify semantic meaning as the relative distribution of like items in a data set.

Stay tuned for more exciting language processing and deep learning resources throughout the spring! 

- Kathryn 

Read more

Newer
Mar 28, 2016 · announcement
Older
Feb 24, 2016 · post

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.
...read more
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).
...read more
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.
...read more
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!
...read more
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.
...read more
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.
...read 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)

Reports

In-depth guides to specific machine learning capabilities

Prototypes

Machine learning prototypes and interactive notebooks
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
Notebook

Interpretability Revisited: SHAP and LIME

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

About

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