08/12/2020

News Aggregation with Diverse Viewpoint Identification Using Neural Embeddings and Semantic Understanding Models

Mark Carlebach, Ria Cheruvu, Brandon Walker, Cesar Ilharco Magalhaes, Sylvain Jaume

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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.

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