AI

Modelling Events through Memory-based, Open-IE Patterns for Abstractive Summarization

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.