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Recent advances in document summarization

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Abstract

The task of automatic document summarization aims at generating short summaries for originally long documents. A good summary should cover the most important information of the original document or a cluster of documents, while being coherent, non-redundant and grammatically readable. Numerous approaches for automatic summarization have been developed to date. In this paper we give a self-contained, broad overview of recent progress made for document summarization within the last 5 years. Specifically, we emphasize on significant contributions made in recent years that represent the state-of-the-art of document summarization, including progress on modern sentence extraction approaches that improve concept coverage, information diversity and content coherence, as well as attempts from summarization frameworks that integrate sentence compression, and more abstractive systems that are able to produce completely new sentences. In addition, we review progress made for document summarization in domains, genres and applications that are different from traditional settings. We also point out some of the latest trends and highlight a few possible future directions.

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Notes

  1. www.summly.com.

  2. However, readers are still assumed to have some basic knowledge in natural language processing and text mining in general.

  3. The tf-idf weighting scheme is a well-known concept in information retrieval that uses the term frequency (tf) in the document for each term and a complementary weight for each term which penalizes terms found in many documents in the collection by using the inverse document frequency (idf), i.e., the inverse of the number of documents that contain the term, as weights.

  4. There is an equivalent definition which provides less intuition in the context of document summarization: f is submodular iff for \(\forall A,B\subseteq V\) we have \(f(A)+f(B)\ge f(A\cup B) + f(A\cap B)\).

  5. A set function f is called monotone, if \(f(A)\le f(B)\) whenever \(A\subseteq B\).

  6. The original paper [116] incorrectly proved a better \((1-1/\sqrt{e})\) bound, as pointed out in a later work from a different research group [134].

  7. Available at http://www.cs.cornell.edu/~rs/sfour/.

  8. Starting from [70], all these papers weirdly evaluate their systems merely on query-focused datasets although they are designed for generic cases.

  9. Nevertheless, in some specific domains and genres such as meeting summarization or opinion summarization, the system has to produce abstractive summaries. We will briefly give some relevant introduction in next section.

  10. That said, designing architectures that actually work is commonly reckoned to be equally labor-intensive.

  11. The authors of [119] use ROUGE-1 recall as the fitness function for measuring summarization quality. The discreteness of objective function (ROUGE) hampers the use of linear programming solutions. In principle, other more advanced and more efficient global optimization techniques such as Bayesian optimization [173] may also be applicable.

  12. For a more specific, comprehensive discussion on opinion summarization, readers may refer to existing survey papers (e.g., [90, 120]).

  13. A scheme of information structure that classifies sentences in scientific text into categories (such as Aim, Background, Own, Contrast and Basis) based on their rhetorical status in scientific discourse.

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Acknowledgements

The work was supported by National Hi-Tech Research and Development Program (863 Program) of China (2015AA015403), National Natural Science Foundation of China (61331011) and IBM Global Faculty Award Program. We would like to thank anonymous reviewers for feedbacks and Jiwei Tan for reporting typos in an earlier draft of this paper.

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  1. Institute of Computer Science and Technology, Peking University, Beijing, 100871, China

    Jin-ge Yao, Xiaojun Wan & Jianguo Xiao

  2. The MOE Key Laboratory of Computational Linguistics, Peking University, Beijing, China

    Jin-ge Yao, Xiaojun Wan & Jianguo Xiao

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Yao, Jg., Wan, X. & Xiao, J. Recent advances in document summarization. Knowl Inf Syst 53, 297–336 (2017). https://doi.org/10.1007/s10115-017-1042-4

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