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TLDR: Extreme Summarization of Scientific Documents

Scientists are often required to process long lists of papers, such as conference proceedings and search engine results, and information overload is becoming an increasing problem for scientists. Titles often don’t convey enough information about the content of a paper, and abstracts are on average over 150 words in length, which can be time-consuming to read in large numbers.

We introduce TLDR generation or the automatic generation of extreme summaries for scientific literature. TLDRs are single-sentence summaries that communicate the main points of a paper and are commonly used on social media and websites like OpenReview.net.

In this talk, I will discuss the challenges of automatically generating TLDRs, including data collection, modeling, evaluation, and implementation, and how we addressed them.

What’s new in Stanford NLP and Stanza

In this talk, I will discuss updates to Stanza, our Python natural language processing toolkit supporting 70 human languages. Compared to existing...