Authors:
Fumiyo Fukumoto
1
;
Yoshimi Suzuki
1
and
Atsuhiro Takasu
2
Affiliations:
1
Univ. of Yamanashi, Japan
;
2
National Institute of Informatics, Japan
Keyword(s):
Topic, Subject, Multi-document Summarization, Moving Average Convergence Divergence.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Artificial Intelligence
;
Data Engineering
;
Enterprise Information Systems
;
Information Systems Analysis and Specification
;
Knowledge Engineering and Ontology Development
;
Knowledge-Based Systems
;
Natural Language Processing
;
Ontologies and the Semantic Web
;
Ontology Engineering
;
Pattern Recognition
;
Symbolic Systems
Abstract:
This paper focuses on continuous news streams and presents a method for detecting salient, key sentences
from stories that discuss the same topic. Our hypothesis about key sentences in multiple stories is that they
include words related to the target topic, and the sub ject of a story. In addition to the TF-IDF term weighting
method, we used the result of assigning domain-specific senses to each word in the story to identify a subject.
A topic, on the other hand, is identified by using a model of ”topic dynamics”. We defined a burst as a time
interval of maximal length over which the rate of change is positive acceleration. We adapted stock market
trend analysis technique, i.e., Moving Average Convergence Divergence (MACD). It shows the relationship
between two moving averages of prices, and is popular indicator of trends in dynamic marketplaces. We
utilized it to measure topic dynamics. The method was tested on the TDT corpora, and the results showed the
effectiveness of the method.