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Extractive Automatic Text Summarization Based on Lexical-Semantic Keywords
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DOI：10.1109/ACCESS.2020.2980226.png)
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摘要
En 中文
The automatic text summarization (ATS) task consists in automatically synthesizing a document to provide a condensed version of it. Creating a summary requires not only selecting the main topics of the sentences but also identifying the key relationships between these topics. Related works rank text units (mainly sentences) to select those that could form the summary. However, the resulting summaries may not include all the topics covered in the source text because important information may have been discarded. In addition, the semantic structure of documents has been barely explored in this field. Thus, this study proposes a new method for the ATS task that takes advantage of semantic information to improve keyword detection. This proposed method increases not only the coverage by clustering the sentences to identify the main topics in the source document but also the precision by detecting the keywords in the clusters. The experimental results of this work indicate that the proposed method outperformed previous methods with a standard collection.
Keyword:
Task analysis
Semantics
Feature extraction
Clustering algorithms
Indexes
Numerical models
Genetic algorithms
Automatic text summarization
cluster validation indexes
genetic algorithm
extractive summaries
topic modelling
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期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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