Modelling Events through Memory-based, Open-IE Patterns for Abstractive Summarization
Venue
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (ACL'14) (2014), pp. 892-901
Publication Year
2014
Authors
Daniele Pighin, Marco Cornolti, Enrique Alfonseca, Katja Filippova
BibTeX
Abstract
Abstractive text summarization of news requires a way of representing events, such
as a collection of pattern clusters in which every cluster represents an event
(e.g., marriage) and every pattern in the cluster is a way of expressing the event
(e.g., X married Y, X and Y tied the knot). We compare three ways of extracting
event patterns: heuristics-based, compression-based and memory-based. While the
former has been used previously in multi-document abstraction, the latter two have
never been used for this task. Compared with the first two techniques, the
memory-based method allows for generating significantly more grammatical and
informative sentences, at the cost of searching a vast space of hundreds of
millions of parse trees of known grammatical utterances. To this end, we introduce
a data structure and a search method that make it possible to efficiently
extrapolate from every sentence the parse sub-trees that match against any of the
stored utterances.
