Key points are not available for this paper at this time.
Recent advances in automatic text summarization have used deep neural networks to generate high-quality abstractive summaries, but the performance of these models strongly depends on large amounts of suitable training data. We propose a new method for mining social media for author-provided summaries, taking advantage of the common practice of appending a "TL;DR" to long posts. A case study using a large Reddit crawl yields the Webis-TLDR-17 corpus, complementing existing corpora primarily from the news genre. Our technique is likely applicable to other social media sites and general web crawls.
Völske et al. (Sun,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: