News Aggregation with Diverse Viewpoint Identification Using Neural Embeddings and Semantic Understanding Models
Mark Carlebach, Ria Cheruvu, Brandon Walker, Cesar Ilharco Magalhaes, Sylvain Jaume
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.- Anthology ID:
- 2020.argmining-1.7
- Volume:
- Proceedings of the 7th Workshop on Argument Mining
- Month:
- December
- Year:
- 2020
- Address:
- Online
- Editors:
- Elena Cabrio, Serena Villata
- Venue:
- ArgMining
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 59–66
- Language:
- URL:
- https://aclanthology.org/2020.argmining-1.7
- DOI:
- Cite (ACL):
- Mark Carlebach, Ria Cheruvu, Brandon Walker, Cesar Ilharco Magalhaes, and Sylvain Jaume. 2020. News Aggregation with Diverse Viewpoint Identification Using Neural Embeddings and Semantic Understanding Models. In Proceedings of the 7th Workshop on Argument Mining, pages 59–66, Online. Association for Computational Linguistics.
- Cite (Informal):
- News Aggregation with Diverse Viewpoint Identification Using Neural Embeddings and Semantic Understanding Models (Carlebach et al., ArgMining 2020)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2020.argmining-1.7.pdf