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).
CITATION STYLE
Chuang, W. T., & Yang, J. (2000). Text summarization by sentence segment extraction using machine learning algorithms. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1805, pp. 454–457). Springer Verlag. https://doi.org/10.1007/3-540-45571-x_52
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