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Evaluating the Factual Consistency of Abstractive Text Summarization


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

4 replies, 714 likes

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: #NLProc

0 replies, 46 likes

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: Key points in thread (1/6):

1 replies, 26 likes

Richard Socher: Keeping the facts straight! Work on Factual Consistency of Abstractive Text Summarization by @iam_wkr @BMarcusMcCann @CaimingXiong and me. Paper: This is really important work for an information society. Nice summary also here:

0 replies, 9 likes

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

Allen Schmaltz: The key point: "Such high levels of factual inconsistency render automatically generated [abstractive] summaries virtually useless in practice."

1 replies, 3 likes

Bryan McCann: More new work with @iam_wkr! This time focusing on evaluation of factual consistency in abstractive text summarization.

0 replies, 3 likes

cathal horan: This is a good example of how to use BERT for #MachineLearning #DeepLearning #NLP classification. The authors use BERT to train a classifier to identify when text summation is factually inconsistent from the source material. #analytics #datascience

0 replies, 1 likes


Found on Oct 29 2019 at

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