| Abstract |
Automatic extraction of semantic information from unstructured text has
always been an important goal of natural language processing. While the
best structure for semantic information is still undecided, graphbased
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 implemented, 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. Different configurations for the semantic graphs are
used and compared, and their fitness for the task discussed.
|
| Citation |
@inproceedings{sevilla_enriched_2016a,
author="Sevilla, Antonio F. G.
and Fern{\'a}ndez-Isabel, Alberto
and D{\'i}az, Alberto",
title="Enriched Semantic Graphs for Extractive Text Summarization",
booktitle="Advances in Artificial Intelligence",
year="2016",
publisher="Springer International Publishing",
pages="217--226",
isbn="978-3-319-44636-3",
}
|