Paper: HEADY: News headline abstraction through event pattern clustering

ACL ID P13-1122
Title HEADY: News headline abstraction through event pattern clustering
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2013
Authors

HEADY: News headline abstraction through event pattern clustering Enrique Alfonseca Google Inc. ealfonseca@google.com Daniele Pighin Google Inc. biondo@google.com Guillermo Garrido? NLP & IR Group at UNED ggarrido@lsi.uned.es Abstract This paper presents HEADY: a novel, ab- stractive approach for headline generation from news collections. From a web-scale corpus of English news, we mine syntac- tic patterns that a Noisy-OR model gener- alizes into event descriptions. At inference time, we query the model with the patterns observed in an unseen news collection, identify the event that better captures the gist of the collection and retrieve the most appropriate pattern to generate a head- line. HEADY improves over a state-of-the- art open-domain title abstraction method, bridging half of the...