Text Summarization by Sentence Segment Extraction Using Machine Learning Algorithms

Authors:
Wesley T. Chuang;Jihoon Yang
Affiliations:
-;-
Venue:
PADKK '00 Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Current Issues and New Applications
Year:
2000

Citing 3
Cited 1

C4.5: programs for machine learning

C4.5: programs for machine learning
The rhetorical parsing, summarization, and generation of natural language texts

The rhetorical parsing, summarization, and generation of natural language texts
The rhetorical parsing, summarization, and generation of natural language texts

The rhetorical parsing, summarization, and generation of natural language texts

Using word sequences for text summarization

TSD'06 Proceedings of the 9th international conference on Text, Speech and Dialogue

Quantified Score

Hi-index	0.00

Visualization

Abstract

We present an approach to the design of an automatic text summarizer that generates a summary by extracting sentence segments. First, sentences are broken into segments by special cue markers. Each segment is represented by a set of predefined features (e.g. location of the segment, number of title words in the segment). Then supervised learning algorithms are used to train the summarizer to extract important sentence segments, based on the feature vector. Results of experiments indicate that the performance of the proposed approach compares quite favorably with other approaches (including MS Word summarizer).