Name
Abstract | ||
|---|---|---|
With the proliferation of the Internet and the huge amount of data it transfers, text summarization is becoming more important. 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, average term frequencies of the words occurring in the segment, number of title words in the segment, and the like). Then a supervised learning algorithm is used to train the summarizer to extract important sentence segments, based on the feature vector. Results of experiments on U.S. patents indicate that the performance of the proposed approach compares very favorably with other approaches (including Microsoft Word summarizer) in terms of precision, recall, and classification accuracy. |
Year | DOI | Venue |
|---|---|---|
2000 | 10.1145/345508.345566 | SIGIR |
Keywords | Field | DocType |
sentence segment extraction,classification accuracy,text summarization,sentence segment,important sentence segment,average term frequency,automatic text summarizer,microsoft word summarizer,extracting sentence segment,u.s. patent,machine learning,feature vector,supervised learning,term frequency | Automatic summarization,Feature vector,Information retrieval,Computer science,Speech recognition,Supervised training,Recall,Sentence,Word processing,The Internet | Conference |
ISBN | Citations | PageRank |
1-58113-226-3 | 69 | 3.57 |
References | Authors | |
7 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wesley T. Chuang | 1 | 97 | 5.74 |
| Jihoon Yang | 2 | 143 | 8.59 |