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        <title>Biased TextRank: Unsupervised Graph-Based Content Extraction</title>
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        <namePart type="given">Ashkan</namePart>
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    <name type="personal">
        <namePart type="given">Rada</namePart>
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    <abstract>We introduce Biased TextRank, a graph-based content extraction method inspired by the popular TextRank algorithm that ranks text spans according to their importance for language processing tasks and according to their relevance to an input “focus.” Biased TextRank enables focused content extraction for text by modifying the random restarts in the execution of TextRank. The random restart probabilities are assigned based on the relevance of the graph nodes to the focus of the task. We present two applications of Biased TextRank: focused summarization and explanation extraction, and show that our algorithm leads to improved performance on two different datasets by significant ROUGE-N score margins. Much like its predecessor, Biased TextRank is unsupervised, easy to implement and orders of magnitude faster and lighter than current state-of-the-art Natural Language Processing methods for similar tasks.</abstract>
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    <identifier type="doi">10.18653/v1/2020.coling-main.144</identifier>
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        <url>https://aclanthology.org/2020.coling-main.144</url>
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        <date>2020-dec</date>
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%0 Conference Proceedings
%T Biased TextRank: Unsupervised Graph-Based Content Extraction
%A Kazemi, Ashkan
%A Pérez-Rosas, Verónica
%A Mihalcea, Rada
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 dec
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F kazemi-etal-2020-biased
%X We introduce Biased TextRank, a graph-based content extraction method inspired by the popular TextRank algorithm that ranks text spans according to their importance for language processing tasks and according to their relevance to an input “focus.” Biased TextRank enables focused content extraction for text by modifying the random restarts in the execution of TextRank. The random restart probabilities are assigned based on the relevance of the graph nodes to the focus of the task. We present two applications of Biased TextRank: focused summarization and explanation extraction, and show that our algorithm leads to improved performance on two different datasets by significant ROUGE-N score margins. Much like its predecessor, Biased TextRank is unsupervised, easy to implement and orders of magnitude faster and lighter than current state-of-the-art Natural Language Processing methods for similar tasks.
%R 10.18653/v1/2020.coling-main.144
%U https://aclanthology.org/2020.coling-main.144
%U https://doi.org/10.18653/v1/2020.coling-main.144
%P 1642-1652

Markdown (Informal)

[Biased TextRank: Unsupervised Graph-Based Content Extraction](https://aclanthology.org/2020.coling-main.144) (Kazemi et al., COLING 2020)

• Biased TextRank: Unsupervised Graph-Based Content Extraction (Kazemi et al., COLING 2020)

ACL

• Ashkan Kazemi, Verónica Pérez-Rosas, and Rada Mihalcea. 2020. Biased TextRank: Unsupervised Graph-Based Content Extraction. In Proceedings of the 28th International Conference on Computational Linguistics, pages 1642–1652, Barcelona, Spain (Online). International Committee on Computational Linguistics.