@inproceedings{volske-etal-2017-tl,
title = "{TL};{DR}: Mining {R}eddit to Learn Automatic Summarization",
author = {V{\"o}lske, Michael and
Potthast, Martin and
Syed, Shahbaz and
Stein, Benno},
editor = "Wang, Lu and
Cheung, Jackie Chi Kit and
Carenini, Giuseppe and
Liu, Fei",
booktitle = "Proceedings of the Workshop on New Frontiers in Summarization",
month = sep,
year = "2017",
address = "Copenhagen, Denmark",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W17-4508",
doi = "10.18653/v1/W17-4508",
pages = "59--63",
abstract = "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 dataset, complementing existing corpora primarily from the news genre. Our technique is likely applicable to other social media sites and general web crawls.",
}
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<abstract>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 dataset, complementing existing corpora primarily from the news genre. Our technique is likely applicable to other social media sites and general web crawls.</abstract>
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%0 Conference Proceedings
%T TL;DR: Mining Reddit to Learn Automatic Summarization
%A Völske, Michael
%A Potthast, Martin
%A Syed, Shahbaz
%A Stein, Benno
%Y Wang, Lu
%Y Cheung, Jackie Chi Kit
%Y Carenini, Giuseppe
%Y Liu, Fei
%S Proceedings of the Workshop on New Frontiers in Summarization
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F volske-etal-2017-tl
%X 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 dataset, complementing existing corpora primarily from the news genre. Our technique is likely applicable to other social media sites and general web crawls.
%R 10.18653/v1/W17-4508
%U https://aclanthology.org/W17-4508
%U https://doi.org/10.18653/v1/W17-4508
%P 59-63
Markdown (Informal)
[TL;DR: Mining Reddit to Learn Automatic Summarization](https://aclanthology.org/W17-4508) (Völske et al., 2017)
ACL
- Michael Völske, Martin Potthast, Shahbaz Syed, and Benno Stein. 2017. TL;DR: Mining Reddit to Learn Automatic Summarization. In Proceedings of the Workshop on New Frontiers in Summarization, pages 59–63, Copenhagen, Denmark. Association for Computational Linguistics.