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Automatic summarization of scientific publications using a feature selection approach

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Abstract

Feature Maximization is a feature selection method that deals efficiently with textual data: to design systems that are altogether language-agnostic, parameter-free and do not require additional corpora to function. We propose to evaluate its use in text summarization, in particular in cases where documents are structured. We first experiment this approach in a single-document summarization context. We evaluate it on the DUC AQUAINT corpus and show that despite the unstructured nature of the corpus, our system is above the baseline and produces encouraging results. We also observe that the produced summaries seem robust to redundancy. Next, we evaluate our method in the more appropriate context of SciSumm challenge, which is dedicated to research publications summarization. These publications are structured in sections and our class-based approach is thus relevant. We more specifically focus on the task that aims to summarize papers using those that refer to them. We consider and evaluate several systems using our approach dealing with specific bag of words. Furthermore, in these systems, we also evaluate cosine and graph-based distance for sentence weighting and comparison. We show that our Feature Maximization based approach performs very well in the SciSumm 2016 context for the considered task, providing better results than the known results so far, and obtaining high recall. We thus demonstrate the flexibility and the relevance of Feature Maximization in this context.

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Notes

  1. The 2nd Computational Linguistics Scientific Document Summarization Shared Task (CL-SciSumm 2016), http://wing.comp.nus.edu.sg/cl-scisumm2016/

  2. In this paper, we always consider only one reference summary, but there may be several ones created by distinct human annotators for example.

  3. The choice of the weighting scheme is not really constrained by the approach instead of producing positive values. Such a scheme is supposed to figure out the significance (i.e., semantic and importance) of the feature for the data. Feature recall is a scale-independent measure but feature predominance is not. We have, however, shown experimentally that the F-measure which is a combination of these two measures is only weakly influenced by feature scaling. Nevertheless, to guarantee full scale-independent behavior for this measure, data may be standardized.

  4. The Document Understanding Conference.

  5. Query-focused summarization.

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Authors and Affiliations

  1. ATILF, Nancy, France

    Hazem Al Saied

  2. LIUM, Université du Maine, Le Mans, France

    Nicolas Dugué

  3. LORIA, SYNALP, Nancy, France

    Jean-Charles Lamirel

Authors
  1. Hazem Al Saied
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  2. Nicolas Dugué
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  3. Jean-Charles Lamirel
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Correspondence to Nicolas Dugué.

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Al Saied, H., Dugué, N. & Lamirel, JC. Automatic summarization of scientific publications using a feature selection approach. Int J Digit Libr 19, 203–215 (2018). https://doi.org/10.1007/s00799-017-0214-x

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  • DOI: https://doi.org/10.1007/s00799-017-0214-x

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