NSTM: Real-time query-driven news overview composition at bloomberg

7Citations
Citations of this article
78Readers
Mendeley users who have this article in their library.

Abstract

Millions of news articles from hundreds of thousands of sources around the globe appear in news aggregators every day. Consuming such a volume of news presents an almost insurmountable challenge. For example, a reader searching on Bloomberg’s system for news about the U.K. would find 10,000 articles on a typical day. Apple Inc., the world’s most journalistically covered company, garners around 1,800 news articles a day. We realized that a new kind of summarization engine was needed, one that would condense large volumes of news into short, easy to absorb points. The system would filter out noise and duplicates to identify and summarize key news about companies, countries or markets. When given a user query, Bloomberg’s solution, Key News Themes (or NSTM), leverages state-of-the-art semantic clustering techniques and novel summarization methods to produce comprehensive, yet concise, digests to dramatically simplify the news consumption process. NSTM is available to hundreds of thousands of readers around the world and serves thousands of requests daily with sub-second latency. At ACL 2020, we will present a demo of NSTM.

Cite

CITATION STYLE

APA

Bambrick, J., Xu, M., Almonte, A., Malioutov, I., Perarnau, G., Selo, V., & Chan, I. C. (2020). NSTM: Real-time query-driven news overview composition at bloomberg. In Proceedings of the Annual Meeting of the Association for Computational Linguistics (pp. 350–361). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2020.acl-demos.40

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free