Text Summarization by Sentence Segment Extraction Using Machine Learning Algorithms
| Autor: | Jihoon Yang, Wesley T. Chuang |
|---|---|
| Rok vydání: | 2000 |
| Předmět: |
business.industry
Computer science Speech recognition Feature vector ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Information processing computer.software_genre Machine learning Automatic summarization Set (abstract data type) Information extraction Artificial intelligence business computer Word (computer architecture) Sentence Natural language processing |
| Zdroj: | Knowledge Discovery and Data Mining. Current Issues and New Applications ISBN: 9783540673828 PAKDD |
| DOI: | 10.1007/3-540-45571-x_52 |
| Popis: | 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). |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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