@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.",
}