Papers of the day   All papers

Evaluating the Factual Consistency of Abstractive Text Summarization

Comments

Oct 29 2019 Richard Socher

Summarization is one of the most important & least solved tasks in #NLProc Problem with all #DeepLearning models: they are not optimized for factual correctness We introduce a new task, dataset and model. Work by @iam_wkr @BMarcusMcCann @CaimingXiong Paper https://arxiv.org/abs/1910.12840 https://t.co/pumDBw4RbO
2 replies, 700 likes


Oct 29 2019 Sebastian Gehrmann

New paper by salesforce on learning a model-based fact checker for Abstractive Summarization. Definitely a much needed evaluation approach, let's hope that these kinds of metrics will become a new standard. Link: https://arxiv.org/abs/1910.12840 #NLProc
0 replies, 32 likes


Oct 29 2019 Wojciech Kryściński

New work in which we approach the problem of evaluating the factual consistency of abstractive summarization models is out! 📫📑 Work w/ @BMarcusMcCann @CaimingXiong @RichardSocher Paper: https://arxiv.org/abs/1910.12840 Key points in thread (1/6): https://t.co/4peXBya1OR
1 replies, 25 likes


Oct 29 2019 MJ

Summarization is such an important NLP task. Imagine a TLDR for everything you read! Talk about time savings and impact.
0 replies, 6 likes


Nov 04 2019 Allen Schmaltz

The key point: "Such high levels of factual inconsistency render automatically generated [abstractive] summaries virtually useless in practice." http://arxiv.org/abs/1910.12840
1 replies, 3 likes


Oct 29 2019 Bryan McCann

More new work with @iam_wkr! This time focusing on evaluation of factual consistency in abstractive text summarization.
0 replies, 3 likes


Content