The SUMMA Platform Prototype

Liepins, Renars; Germann, Ulrich; Bārzdiņš, Guntis; Birch, Alexandra; Renals, Steve; Susanne, Weber,; Kreeft, Peggy van der; Bourlard, Hervé; Prieto, João; Klejch, Ondřej; Bell, P. J.; Lazaridis, Alexandros; Mendes, Afonso; Riedel, Sebastian; Almeida, Mariana S. C.; Balage, Pedro; Cohen, Shay B.; Dwojak, Tomasz; Garner, Philip N.; Giefer, Andreas; Junczys-Dowmunt, Marcin; Imran, Hina; Nogueira, David Silva; Ali, Ahmed; Miranda, Sebastião; Popescu-Belis, Andréi; Werlen, Lesly Miculicich; Papasarantopoulos, Nikos; Obamuyide, Abiola; Jones, Clive; Dalvi, Fahim; Vlachos, Andreas; Wang, Yang; Tong, Sibo; Sennrich, Rico; Pappas, Nikolaos; Narayan, Shashi; Damonte, Marco; Durrani, Nadir; Khurana, Sameer; Abdelali, Ahmed; Sajjad, Hassan; Vogel, Stephan; Sheppey, David; Chris, Hernon,; Mitchell, Jeff

doi:10.18653/v1/e17-3029

Cited by 2 publications

(2 citation statements)

References 6 publications

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“…Finally, we discussed how to leverage different SVM training procedures for ranking and classification to improve monolingual and crosslingual clustering decisions. Our system is integrated in a larger media monitoring project (Liepins et al, 2017;Germann et al, 2018) and solving the usecases of monitors and journalists, having been validated with qualitative user testing.…”

Section: Related Workmentioning

confidence: 99%

Multilingual Clustering of Streaming News

Miranda¹,

Znotiņš²,

Cohen³

et al. 2018

Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

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Clustering news across languages enables efficient media monitoring by aggregating articles from multilingual sources into coherent stories. Doing so in an online setting allows scalable processing of massive news streams. To this end, we describe a novel method for clustering an incoming stream of multilingual documents into monolingual and crosslingual story clusters. Unlike typical clustering approaches that consider a small and known number of labels, we tackle the problem of discovering an ever growing number of cluster labels in an online fashion, using real news datasets in multiple languages. Our method is simple to implement, computationally efficient and produces state-of-the-art results on datasets in German, English and Spanish.

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Section: Related Workmentioning

confidence: 99%

Multilingual Clustering of Streaming News

Miranda¹,

Znotiņš²,

Cohen³

et al. 2018

Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Self Cite

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“…SUMMA (Liepins et al, 2017) is another comparable system which integrates a variety of NLP components and provides support for numerous media and languages, to simultaneously monitor several media broadcasts. SUMMA applies the online clustering algorithm by Aggarwal and Yu (2006) and the extractive summarization algorithm by Almeida and Martins (2013).…”

Section: Related Workmentioning

confidence: 99%

NSTM: Real-Time Query-Driven News Overview Composition at Bloomberg

Bambrick¹,

Xu²,

Almonte³

et al. 2020

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations

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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. * Order reflects writing contributions; M.X., I.C.C., and J.B. designed and developed a prototype of the system; All implemented the production system; A.A. managed the project. I.C.C. worked on the project while employed by Bloomberg.

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The SUMMA Platform Prototype

Cited by 2 publications

References 6 publications

Multilingual Clustering of Streaming News

Multilingual Clustering of Streaming News

NSTM: Real-Time Query-Driven News Overview Composition at Bloomberg

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