In news articles the lead bias is a common phenomenon that usually dominates the learning signals for neural extractive summarizers, severely limiting their performance on data with different or even no bias. In this paper, we introduce a novel technique to demote lead bias and make the summarizer focus more on the content semantics. Experiments on two news corpora with different degrees of lead bias show that our method can effectively demote the model’s learned lead bias and improve its generality on out-of-distribution data, with little to no performance loss on in-distribution data.
Linzi Xing, Wen Xiao, and Giuseppe Carenini. 2021.Demoting the Lead Bias in News Summarization via Alternating Adversarial Learning. InProceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pages 948–954, Online. Association for Computational Linguistics.
@inproceedings{xing-etal-2021-demoting, title = "Demoting the Lead Bias in News Summarization via Alternating Adversarial Learning", author = "Xing, Linzi and Xiao, Wen and Carenini, Giuseppe", editor = "Zong, Chengqing and Xia, Fei and Li, Wenjie and Navigli, Roberto", booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)", month = aug, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.acl-short.119/", doi = "10.18653/v1/2021.acl-short.119", pages = "948--954", abstract = "In news articles the lead bias is a common phenomenon that usually dominates the learning signals for neural extractive summarizers, severely limiting their performance on data with different or even no bias. In this paper, we introduce a novel technique to demote lead bias and make the summarizer focus more on the content semantics. Experiments on two news corpora with different degrees of lead bias show that our method can effectively demote the model`s learned lead bias and improve its generality on out-of-distribution data, with little to no performance loss on in-distribution data."}
<?xml version="1.0" encoding="UTF-8"?><modsCollection xmlns="http://www.loc.gov/mods/v3"><mods ID="xing-etal-2021-demoting"> <titleInfo> <title>Demoting the Lead Bias in News Summarization via Alternating Adversarial Learning</title> </titleInfo> <name type="personal"> <namePart type="given">Linzi</namePart> <namePart type="family">Xing</namePart> <role> <roleTerm authority="marcrelator" type="text">author</roleTerm> </role> </name> <name type="personal"> <namePart type="given">Wen</namePart> <namePart type="family">Xiao</namePart> <role> <roleTerm authority="marcrelator" type="text">author</roleTerm> </role> </name> <name type="personal"> <namePart type="given">Giuseppe</namePart> <namePart type="family">Carenini</namePart> <role> <roleTerm authority="marcrelator" type="text">author</roleTerm> </role> </name> <originInfo> <dateIssued>2021-08</dateIssued> </originInfo> <typeOfResource>text</typeOfResource> <relatedItem type="host"> <titleInfo> <title>Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)</title> </titleInfo> <name type="personal"> <namePart type="given">Chengqing</namePart> <namePart type="family">Zong</namePart> <role> <roleTerm authority="marcrelator" type="text">editor</roleTerm> </role> </name> <name type="personal"> <namePart type="given">Fei</namePart> <namePart type="family">Xia</namePart> <role> <roleTerm authority="marcrelator" type="text">editor</roleTerm> </role> </name> <name type="personal"> <namePart type="given">Wenjie</namePart> <namePart type="family">Li</namePart> <role> <roleTerm authority="marcrelator" type="text">editor</roleTerm> </role> </name> <name type="personal"> <namePart type="given">Roberto</namePart> <namePart type="family">Navigli</namePart> <role> <roleTerm authority="marcrelator" type="text">editor</roleTerm> </role> </name> <originInfo> <publisher>Association for Computational Linguistics</publisher> <place> <placeTerm type="text">Online</placeTerm> </place> </originInfo> <genre authority="marcgt">conference publication</genre> </relatedItem> <abstract>In news articles the lead bias is a common phenomenon that usually dominates the learning signals for neural extractive summarizers, severely limiting their performance on data with different or even no bias. In this paper, we introduce a novel technique to demote lead bias and make the summarizer focus more on the content semantics. Experiments on two news corpora with different degrees of lead bias show that our method can effectively demote the model‘s learned lead bias and improve its generality on out-of-distribution data, with little to no performance loss on in-distribution data.</abstract> <identifier type="citekey">xing-etal-2021-demoting</identifier> <identifier type="doi">10.18653/v1/2021.acl-short.119</identifier> <location> <url>https://aclanthology.org/2021.acl-short.119/</url> </location> <part> <date>2021-08</date> <extent unit="page"> <start>948</start> <end>954</end> </extent> </part></mods></modsCollection>
%0 Conference Proceedings%T Demoting the Lead Bias in News Summarization via Alternating Adversarial Learning%A Xing, Linzi%A Xiao, Wen%A Carenini, Giuseppe%Y Zong, Chengqing%Y Xia, Fei%Y Li, Wenjie%Y Navigli, Roberto%S Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)%D 2021%8 August%I Association for Computational Linguistics%C Online%F xing-etal-2021-demoting%X In news articles the lead bias is a common phenomenon that usually dominates the learning signals for neural extractive summarizers, severely limiting their performance on data with different or even no bias. In this paper, we introduce a novel technique to demote lead bias and make the summarizer focus more on the content semantics. Experiments on two news corpora with different degrees of lead bias show that our method can effectively demote the model‘s learned lead bias and improve its generality on out-of-distribution data, with little to no performance loss on in-distribution data.%R 10.18653/v1/2021.acl-short.119%U https://aclanthology.org/2021.acl-short.119/%U https://doi.org/10.18653/v1/2021.acl-short.119%P 948-954
[Demoting the Lead Bias in News Summarization via Alternating Adversarial Learning](https://aclanthology.org/2021.acl-short.119/) (Xing et al., ACL-IJCNLP 2021)
Linzi Xing, Wen Xiao, and Giuseppe Carenini. 2021.Demoting the Lead Bias in News Summarization via Alternating Adversarial Learning. InProceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pages 948–954, Online. Association for Computational Linguistics.