TLDR: AI for Text Summarization and Generation of TLDRs

Published in Towards Data Science

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TLDR (or TL;DR) is a common internet acronym for “Too Long; Didn’t Read.” It likely originated on the comedy forum Something Awful around 2002 (source) and then became more popular in online forums like Reddit. It is often used in social media where the author or commenters summarise lengthy posts and provide a TLDR summary of one or two lines as a courtesy to other readers. TLDRs help readers get the gist of the information and enable quick informed decisions on whether to invest the time in reading the full post.
With Natural language processing (NLP) and automatic text summarization systems, TLDR generation can be automated. Automatic text summarization is a challenging problem of generating a shorter summary of a long document while preserving its essence. It has wide practical applications in multiple domains such as legal contract analysis, search (summarising use information in websites, entity-centric summarization from Wikipedia articles), question answering systems, media (generating news headlines, summarising articles in newsletters), marketing (generating copy, slogans) among others. Automatically generated text summaries help reduce reading time, are non-biased compared to human authored summaries, and could also be beneficial for a lot of personal day to day applications like email summarization, TLDR generation for posting on social media sites like Twitter, and more.
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