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2018 | OriginalPaper | Buchkapitel

Semantic WordRank: Generating Finer Single-Document Summarizations

verfasst von : Hao Zhang, Jie Wang

Erschienen in: Intelligent Data Engineering and Automated Learning – IDEAL 2018

Verlag: Springer International Publishing

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Abstract

We present Semantic WordRank (SWR), an unsupervised method for generating an extractive summary of a single document. Built on a weighted word graph with semantic and co-occurrence edges, SWR scores sentences using an article-structure-biased PageRank algorithm with a Softplus function adjustment, and promotes topic diversity using spectral subtopic clustering under the Word-Movers-Distance metric. We evaluate SWR on the DUC-02 and SummBank datasets and show that SWR produces better summaries than the state-of-the-art algorithms over DUC-02 under common ROUGE measures. We then show that, under the same measures over SummBank, SWR outperforms each of the three human annotators (aka. judges) and compares favorably with the combined performance of all judges.

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Vorheriges Kapitel An Adaptive Anomaly Detection Algorithm for Periodic Data Streams

Nächstes Kapitel Exploratory Study of the Effects of Cardiac Murmurs on Electrocardiographic-Signal-Based Biometric Systems

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Titel: Semantic WordRank: Generating Finer Single-Document Summarizations
verfasst von: Hao Zhang
Jie Wang
Verlag: Springer International Publishing
Buch: Intelligent Data Engineering and Automated Learning – IDEAL 2018
Print ISBN: 978-3-030-03492-4

Electronic ISBN: 978-3-030-03493-1

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-030-03493-1_42

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