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        <title>Evaluating text coherence based on semantic similarity graph</title>
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        <namePart type="given">Jan</namePart>
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            <title>Proceedings of TextGraphs-11: the Workshop on Graph-based Methods for Natural Language Processing</title>
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        <name type="personal">
            <namePart type="given">Martin</namePart>
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    <abstract>Coherence is a crucial feature of text because it is indispensable for conveying its communication purpose and meaning to its readers. In this paper, we propose an unsupervised text coherence scoring based on graph construction in which edges are established between semantically similar sentences represented by vertices. The sentence similarity is calculated based on the cosine similarity of semantic vectors representing sentences. We provide three graph construction methods establishing an edge from a given vertex to a preceding adjacent vertex, to a single similar vertex, or to multiple similar vertices. We evaluated our methods in the document discrimination task and the insertion task by comparing our proposed methods to the supervised (Entity Grid) and unsupervised (Entity Graph) baselines. In the document discrimination task, our method outperformed the unsupervised baseline but could not do the supervised baseline, while in the insertion task, our method outperformed both baselines.</abstract>
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    <identifier type="doi">10.18653/v1/W17-2410</identifier>
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%0 Conference Proceedings
%T Evaluating text coherence based on semantic similarity graph
%A Putra, Jan Wira Gotama
%A Tokunaga, Takenobu
%Y Riedl, Martin
%Y Somasundaran, Swapna
%Y Glavaš, Goran
%Y Hovy, Eduard
%S Proceedings of TextGraphs-11: the Workshop on Graph-based Methods for Natural Language Processing
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, Canada
%F putra-tokunaga-2017-evaluating
%X Coherence is a crucial feature of text because it is indispensable for conveying its communication purpose and meaning to its readers. In this paper, we propose an unsupervised text coherence scoring based on graph construction in which edges are established between semantically similar sentences represented by vertices. The sentence similarity is calculated based on the cosine similarity of semantic vectors representing sentences. We provide three graph construction methods establishing an edge from a given vertex to a preceding adjacent vertex, to a single similar vertex, or to multiple similar vertices. We evaluated our methods in the document discrimination task and the insertion task by comparing our proposed methods to the supervised (Entity Grid) and unsupervised (Entity Graph) baselines. In the document discrimination task, our method outperformed the unsupervised baseline but could not do the supervised baseline, while in the insertion task, our method outperformed both baselines.
%R 10.18653/v1/W17-2410
%U https://aclanthology.org/W17-2410
%U https://doi.org/10.18653/v1/W17-2410
%P 76-85

Markdown (Informal)

[Evaluating text coherence based on semantic similarity graph](https://aclanthology.org/W17-2410) (Putra & Tokunaga, TextGraphs 2017)

• Evaluating text coherence based on semantic similarity graph (Putra & Tokunaga, TextGraphs 2017)

ACL

• Jan Wira Gotama Putra and Takenobu Tokunaga. 2017. Evaluating text coherence based on semantic similarity graph. In Proceedings of TextGraphs-11: the Workshop on Graph-based Methods for Natural Language Processing, pages 76–85, Vancouver, Canada. Association for Computational Linguistics.