Paper Summary: Text Summarization Techniques: A Brief Survey

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Please note This post is mainly intended for my personal use. It is not peer-reviewed work and should not be taken as such.

overview-of-text-summarization Overview of automatic text
summarization strategies.
This article focuses on extractive approaches only.


Surveys extractive text summarization techniques.


There are two distinct approaches to automatic text summarization: extractive and abstractive approaches.

  • Extractive approaches: Approaches that identify important sections/sentences/phrases and select them.

  • Abstractive approaches: Actually parses the text semantically and tries to generate a passing summarization that may or may not include actual sentences from the original text. A much harder problem, more similar to what humans actually do.

All extractive summarization approaches perform the following three tasks:

  • 1) Construct an intermediate represention of the text

  • 2) Scores candidate sentences/phrases based upon the representation found on step 1)

  • 3) Builds a summary containing a desired number of sentences (probably ranked by score).


  • "Even though summaries created by humans are usually not extractive, most of the summarization research today has focused on extractive summarization"


ROUGE is a metric used to compare two blocks of text. It can be used to evaluate automatically generated summaries with respect to ground truth (original summaries, titles, etc).

$$ \text{ROUGE}_n=\frac{p}{q} $$

Where \(p\) is the number of common \(n\)-grams occurring both in the generated and in the original summary, and \(q\) is the number of \(n\)-grams in the original summary.


  • The earliest article on automatic text summarization seems to be 1958 with Luhn: "The automatic creation of literature abstracts"


Dialogue & Discussion