Reaching for upper bound ROUGE score of extractive summarization methods
Akhmetov I. Mussabayev R. Gelbukh A.
2022PeerJ Inc.
PeerJ Computer Science
2022#8
The extractive text summarization (ETS) method for finding the salient information from a text automatically uses the exact sentences from the source text. In this article, we answer the question of what quality of a summary we can achieve with ETS methods? To maximize the ROUGE-1 score, we used five approaches: (1) adapted reduced variable neighborhood search (RVNS), (2) Greedy algorithm, (3) VNS initialized by Greedy algorithm results, (4) genetic algorithm, and (5) genetic algorithm initialized by the Greedy algorithm results. Furthermore, we ran experiments on articles from the arXive dataset. As a result, we found 0.59 and 0.25 scores for ROUGE-1 and ROUGE-2, respectively achievable by the approach, where the genetic algorithm initialized by the Greedy algorithm results, which happens to yield the best results out of the tested approaches. Moreover, those scores appear to be higher than scores obtained by the current state-of-the-art text summarization models: the best score in the literature for ROUGE-1 on the same data set is 0.46. Therefore, we have room for the development of ETS methods, which are now undeservedly forgotten.
Genetic algorithm , Greedy algorithm , Rouge , Text summarization , Variable neighborhood search
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Kazakh-British Technical University, Almaty, Kazakhstan
Institute of Information and Computational Technologies, Almaty, Kazakhstan
Instituto Politecnico Nacional, Mexico, Mexico
Kazakh-British Technical University
Institute of Information and Computational Technologies
Instituto Politecnico Nacional
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