Extractive Summarization Considering Discourse and Coreference Relations based on Heterogeneous Graph
Yin Jou Huang, Sadao Kurohashi
Abstract
Modeling the relations between text spans in a document is a crucial yet challenging problem for extractive summarization. Various kinds of relations exist among text spans of different granularity, such as discourse relations between elementary discourse units and coreference relations between phrase mentions. In this paper, we propose a heterogeneous graph based model for extractive summarization that incorporates both discourse and coreference relations. The heterogeneous graph contains three types of nodes, each corresponds to text spans of different granularity. Experimental results on a benchmark summarization dataset verify the effectiveness of our proposed method.
Topics & Concepts
CoreferenceAutomatic summarizationComputer scienceGranularityGraphNatural language processingPhraseBenchmark (surveying)Artificial intelligenceInformation retrievalResolution (logic)Theoretical computer scienceProgramming languageGeodesyGeographyTopic ModelingNatural Language Processing TechniquesAdvanced Text Analysis Techniques