Automatic Summarization in Chinese Product Reviews
Abstract: With the increasing
number of online comments, it was hard for buyers to find useful information in
a short time so it made sense to do research on automatic summarization which
fundamental work was focused on product reviews mining. Previous studies mainly
focused on explicit features extraction whereas often ignored implicit features
which hadn't been stated clearly but containing necessary information for
analyzing comments. So how to quickly and accurately mine features from web
reviews had important significance for summarization technology. In this paper,
explicit features and “feature-opinion” pairs in the explicit sentences were
extracted by Conditional Random Field and implicit product features were
recognized by a bipartite graph model based on random walk algorithm. Then
incorporating features and corresponding opinions into a structured text and
the abstract were generated based on the extraction results. The experiment
results demonstrated the proposed methods out preferred baselines.
Keywords: bipartite graph,
implicit features, conditional random field, random walk algorithm, automatic summarization
Author: Lizhen Liu, Wandi Du,
Hanshi Wang, Wei Song
Journal Code: jptkomputergg170172
