@inproceedings{carlebach-etal-2020-news,
title = "News Aggregation with Diverse Viewpoint Identification Using Neural Embeddings and Semantic Understanding Models",
author = "Carlebach, Mark and
Cheruvu, Ria and
Walker, Brandon and
Ilharco Magalhaes, Cesar and
Jaume, Sylvain",
editor = "Cabrio, Elena and
Villata, Serena",
booktitle = "Proceedings of the 7th Workshop on Argument Mining",
month = dec,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.argmining-1.7/",
pages = "59--66",
abstract = "Today`s news volume makes it impractical for readers to get a diverse and comprehensive view of published articles written from opposing viewpoints. We introduce a transformer-based news aggregation system, composed of topic modeling, semantic clustering, claim extraction, and textual entailment that identifies viewpoints presented in articles within a semantic cluster and classifies them into positive, neutral and negative entailments. Our novel embedded topic model using BERT-based embeddings outperforms baseline topic modeling algorithms by an 11{\%} relative improvement. We compare recent semantic similarity models in the context of news aggregation, evaluate transformer-based models for claim extraction on news data, and demonstrate the use of textual entailment models for diverse viewpoint identification."
}
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<abstract>Today‘s news volume makes it impractical for readers to get a diverse and comprehensive view of published articles written from opposing viewpoints. We introduce a transformer-based news aggregation system, composed of topic modeling, semantic clustering, claim extraction, and textual entailment that identifies viewpoints presented in articles within a semantic cluster and classifies them into positive, neutral and negative entailments. Our novel embedded topic model using BERT-based embeddings outperforms baseline topic modeling algorithms by an 11% relative improvement. We compare recent semantic similarity models in the context of news aggregation, evaluate transformer-based models for claim extraction on news data, and demonstrate the use of textual entailment models for diverse viewpoint identification.</abstract>
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%0 Conference Proceedings
%T News Aggregation with Diverse Viewpoint Identification Using Neural Embeddings and Semantic Understanding Models
%A Carlebach, Mark
%A Cheruvu, Ria
%A Walker, Brandon
%A Ilharco Magalhaes, Cesar
%A Jaume, Sylvain
%Y Cabrio, Elena
%Y Villata, Serena
%S Proceedings of the 7th Workshop on Argument Mining
%D 2020
%8 December
%I Association for Computational Linguistics
%C Online
%F carlebach-etal-2020-news
%X Today‘s news volume makes it impractical for readers to get a diverse and comprehensive view of published articles written from opposing viewpoints. We introduce a transformer-based news aggregation system, composed of topic modeling, semantic clustering, claim extraction, and textual entailment that identifies viewpoints presented in articles within a semantic cluster and classifies them into positive, neutral and negative entailments. Our novel embedded topic model using BERT-based embeddings outperforms baseline topic modeling algorithms by an 11% relative improvement. We compare recent semantic similarity models in the context of news aggregation, evaluate transformer-based models for claim extraction on news data, and demonstrate the use of textual entailment models for diverse viewpoint identification.
%U https://aclanthology.org/2020.argmining-1.7/
%P 59-66
Markdown (Informal)
[News Aggregation with Diverse Viewpoint Identification Using Neural Embeddings and Semantic Understanding Models](https://aclanthology.org/2020.argmining-1.7/) (Carlebach et al., ArgMining 2020)
ACL