@inproceedings{kumar-cheung-2019-understanding,
title = "{U}nderstanding the {B}ehaviour of {N}eural {A}bstractive {S}ummarizers using {C}ontrastive {E}xamples",
author = "Kumar, Krtin and
Cheung, Jackie Chi Kit",
editor = "Burstein, Jill and
Doran, Christy and
Solorio, Thamar",
booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N19-1396/",
doi = "10.18653/v1/N19-1396",
pages = "3949--3954",
abstract = "Neural abstractive summarizers generate summary texts using a language model conditioned on the input source text, and have recently achieved high ROUGE scores on benchmark summarization datasets. We investigate how they achieve this performance with respect to human-written gold-standard abstracts, and whether the systems are able to understand deeper syntactic and semantic structures. We generate a set of contrastive summaries which are perturbed, deficient versions of human-written summaries, and test whether existing neural summarizers score them more highly than the human-written summaries. We analyze their performance on different datasets and find that these systems fail to understand the source text, in a majority of the cases."
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<abstract>Neural abstractive summarizers generate summary texts using a language model conditioned on the input source text, and have recently achieved high ROUGE scores on benchmark summarization datasets. We investigate how they achieve this performance with respect to human-written gold-standard abstracts, and whether the systems are able to understand deeper syntactic and semantic structures. We generate a set of contrastive summaries which are perturbed, deficient versions of human-written summaries, and test whether existing neural summarizers score them more highly than the human-written summaries. We analyze their performance on different datasets and find that these systems fail to understand the source text, in a majority of the cases.</abstract>
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%0 Conference Proceedings
%T Understanding the Behaviour of Neural Abstractive Summarizers using Contrastive Examples
%A Kumar, Krtin
%A Cheung, Jackie Chi Kit
%Y Burstein, Jill
%Y Doran, Christy
%Y Solorio, Thamar
%S Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F kumar-cheung-2019-understanding
%X Neural abstractive summarizers generate summary texts using a language model conditioned on the input source text, and have recently achieved high ROUGE scores on benchmark summarization datasets. We investigate how they achieve this performance with respect to human-written gold-standard abstracts, and whether the systems are able to understand deeper syntactic and semantic structures. We generate a set of contrastive summaries which are perturbed, deficient versions of human-written summaries, and test whether existing neural summarizers score them more highly than the human-written summaries. We analyze their performance on different datasets and find that these systems fail to understand the source text, in a majority of the cases.
%R 10.18653/v1/N19-1396
%U https://aclanthology.org/N19-1396/
%U https://doi.org/10.18653/v1/N19-1396
%P 3949-3954
Markdown (Informal)
[Understanding the Behaviour of Neural Abstractive Summarizers using Contrastive Examples](https://aclanthology.org/N19-1396/) (Kumar & Cheung, NAACL 2019)
ACL