Enriched Semantic Graphs for Extractive Text Summarization, Antonio F. G. Sevilla, Alberto Fernández-Isabel, Alberto Díaz, in The Conference of the Spanish Association for AI (CAEPIA 2016), published by Springer in LNAI vol. 9868. Available on Springer link.


Automatic extraction of semantic information from unstruc- tured text has always been an important goal of natural language pro- cessing. While the best structure for semantic information is still unde- cided, graph-based representations enjoy a healthy following. Some of these representations are extracted directly from the text and external knowledge, while others are built from linguistic insight, created from the deep analysis of the surface text. In this document a combination of both approaches is outlined, and its application for extractive text summarization is described. A pipeline for this task has been imple- mented, and its results evaluated against a collection of documents from the DUC2003 competition. Graph construction is fully automatic, and summary creation is based on the clustering of conceptual nodes. Differ- ent configurations for the semantic graphs are used and compared, and their fitness for the task discussed.