Grouping business news stories based on salience of named entities

Llorenç Escoter, Lidia Pivovarova, Mian Du, Anisia Katinskaia, Roman Yangarber


Abstract
In news aggregation systems focused on broad news domains, certain stories may appear in multiple articles. Depending on the relative importance of the story, the number of versions can reach dozens or hundreds within a day. The text in these versions may be nearly identical or quite different. Linking multiple versions of a story into a single group brings several important benefits to the end-user–reducing the cognitive load on the reader, as well as signaling the relative importance of the story. We present a grouping algorithm, and explore several vector-based representations of input documents: from a baseline using keywords, to a method using salience–a measure of importance of named entities in the text. We demonstrate that features beyond keywords yield substantial improvements, verified on a manually-annotated corpus of business news stories.
Anthology ID:
E17-1103
Volume:
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers
Month:
April
Year:
2017
Address:
Valencia, Spain
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1096–1106
Language:
URL:
https://www.aclweb.org/anthology/E17-1103
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/E17-1103.pdf