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Personalized, Aspect-based Summarization of Movie Reviews

Abstract : Online reviewing websites help users decide what to buy or places to go. These platforms allow users to express their opinions using numerical ratings as well as textual comments. The numerical ratings give a coarse idea of the service. On the other hand, textual comments give full details which is tedious for users to read. In this dissertation, we develop novel methods and algorithms to generate personalized, aspect-based summaries of movie reviews for a given user. The first problem we tackle is extracting a set of related words to an aspect from movie reviews. Our evaluation shows that our method is able to extract even unpopular terms that represent an aspect, such as compound terms or abbreviations, as opposed to the methods from the related work. We then study the problem of annotating sentences with aspects, and propose a new method that annotates sentences based on a similarity between the aspect signature and the terms in the sentence. The third problem we tackle is the generation of personalized, aspect-based summaries. We propose an optimization algorithm to maximize the coverage of the aspects the user is interested in and the representativeness of sentences in the summary subject to a length and similarity constraints. Finally, we perform three user studies that show that the approach we propose outperforms the state of art method for generating summaries.
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Contributor : Abes Star :  Contact
Submitted on : Wednesday, April 28, 2021 - 1:01:49 AM
Last modification on : Thursday, March 17, 2022 - 4:55:40 PM


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  • HAL Id : tel-02444980, version 2


Sara El Aouad. Personalized, Aspect-based Summarization of Movie Reviews. Artificial Intelligence [cs.AI]. Sorbonne Université, 2019. English. ⟨NNT : 2019SORUS019⟩. ⟨tel-02444980v2⟩



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