![]() ZDNET's editorial team writes on behalf of you, our reader. Indeed, we follow strict guidelines that ensure our editorial content is never influenced by advertisers. Neither ZDNET nor the author are compensated for these independent reviews. This helps support our work, but does not affect what we cover or how, and it does not affect the price you pay. When you click through from our site to a retailer and buy a product or service, we may earn affiliate commissions. And we pore over customer reviews to find out what matters to real people who already own and use the products and services we’re assessing. We gather data from the best available sources, including vendor and retailer listings as well as other relevant and independent reviews sites. Our technique is likely applicable to other social media sites and general web crawls.ZDNET's recommendations are based on many hours of testing, research, and comparison shopping. A case study using a large Reddit crawl yields the Webis-TLDR-17 dataset, complementing existing corpora primarily from the news genre. 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. %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. %I Association for Computational Linguistics %S Proceedings of the Workshop on New Frontiers in Summarization %T TL DR: Mining Reddit to Learn Automatic Summarization Our technique is likely applicable to other social media sites and general web crawls. 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. Proceedings of the Workshop on New Frontiers in SummarizationĪssociation for Computational Linguistics TL DR: Mining Reddit to Learn Automatic Summarization Our technique is likely applicable to other social media sites and general web crawls.", ![]() Cite (Informal): TL DR: Mining Reddit to Learn Automatic Summarization (Völske et al., 2017) Copy Citation: BibTeX Markdown MODS XML Endnote More options… PDF: Data Webis-TLDR-17 = " to long posts. ![]() Association for Computational Linguistics. In Proceedings of the Workshop on New Frontiers in Summarization, pages 59–63, Copenhagen, Denmark. TL DR: Mining Reddit to Learn Automatic Summarization. Anthology ID: W17-4508 Volume: Proceedings of the Workshop on New Frontiers in Summarization Month: September Year: 2017 Address: Copenhagen, Denmark Editors: Lu Wang,įei Liu Venue: WS SIG: Publisher: Association for Computational Linguistics Note: Pages: 59–63 Language: URL: DOI: 10.18653/v1/W17-4508 Bibkey: volske-etal-2017-tl Cite (ACL): Michael Völske, Martin Potthast, Shahbaz Syed, and Benno Stein. ![]() 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.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |