Abstract
Authors often re-purpose existing content to create shorter versions for other channels. Automatic summarization techniques can be used to generate a candidate content that can be further fine-tuned by the author. Existing work in automatic summarization primarily focus on providing a single succinct summary. However, this may not suit the needs of a content author or curator, who may want to repurpose/select the content from several alternative candidates. In this paper, we propose an approach to generate multiple diverse summaries, so that authors can choose an appropriate summary without compromising on the summary quality. Our approach can be utilized in conjunction with a large class of extractive summarization techniques, and we illustrate our approach with several summarization techniques. We experimentally show that our approach results in fairly diverse summaries, without compromising the quality of the summaries with respect to the single summary generated by the corresponding base methods.
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References
Carbonell, J.G., Goldstein, J.: The use of MMR, diversity-based reranking for reordering documents and producing summaries. In: Research and Development in Information Retrieval, pp. 335–336 (1998)
Erkan, G., Radev, D.R.: LexRank: graph-based lexical centrality as salience in text summarization. J. Artif. Intell. Res. 22, 457–479 (2004)
Ganesan, K., Zhai, C., Han, J.: A graph-based approach to abstractive summarization of highly redundant opinions. In: Association for Computational Linguistics, pp. 340–348 (2010)
Khabiri, E., Hsu, C., Caverlee, J.: Analyzing and predicting community preference of socially generated metadata: a case study on comments in the digg community. In: ICWSM (2009)
Louis, A., Nenkova, A.: Automatically evaluating content selection in summarization without human models. In: EMNLP, pp. 306–314 (2009)
Mei, Q., Guo, J., Radev, D.R.: Divrank: the interplay of prestige and diversity in information networks. In: Rao, B., Krishnapuram, B., Tomkins, A., Yang, Q. (eds.) KDD, pp. 1009–1018. ACM (2010)
Mihalcea, R.: Language independent extractive summarization. ACLdemo, pp. 49–52 (2005)
Modani, N., Khabiri, E., Srinivasan, H., Caverlee, J.: Creating diverse product review summaries: a graph approach. In: Wang, J., Cellary, W., Wang, D., Wang, H., Chen, S.-C., Li, T., Zhang, Y. (eds.) WISE 2015. LNCS, vol. 9418, pp. 169–184. Springer, Heidelberg (2015). doi:10.1007/978-3-319-26190-4_12
Pemantle, R.: Vertex-reinforced random walk. Probab. Theory Relat. Fields 92(1), 117–136 (1992)
Tong, H., He, J., Wen, Z., Konuru, R., Lin, C.Y.: Diversified ranking on large graphs: an optimization viewpoint. In: KDD, pp. 1028–1036. ACM (2011)
Wu, J., Xu, B., Li, S.: An unsupervised approach to rank product reviews. In: FSKD, pp. 1769–1772 (2011)
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Modani, N., Srinivasan, B.V., Jhamtani, H. (2016). Generating Multiple Diverse Summaries. In: Cellary, W., Mokbel, M., Wang, J., Wang, H., Zhou, R., Zhang, Y. (eds) Web Information Systems Engineering – WISE 2016. WISE 2016. Lecture Notes in Computer Science(), vol 10041. Springer, Cham. https://doi.org/10.1007/978-3-319-48740-3_13
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DOI: https://doi.org/10.1007/978-3-319-48740-3_13
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