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Automatic Training Data Construction and Extractive Supervised Summarization for NTCIR-14 QA Lab-PoliInfo

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Part of the Lecture Notes in Computer Science book series (LNISA,volume 11966)

Abstract

On the summarization task at NTCIR-14 QA Lab-PoliInfo, participants of the task need to generate a summary corresponding to an assembly member speech in assembly minutes within the limit length. Our method extracts important sentences to summarize an assembly member speech in the minutes. Our method applies a machine learning model to predict the important sentences. However, the given assembly minutes’ data do not contain information about the importance of the sentences. As a result, we cannot directly utilize machine learning techniques for the task. Therefore, we construct training data for the importance prediction model using a word similarity between sentences in a speech and those in the summary. In addition, we apply the sentence reduction process. In the process, we consider characteristics of summaries of assembly minutes to avoid removal of important words in extracted sentences. On the evaluation, all the scores by our supervised method with the constructed data outperformed unsupervised and supervised baseline methods. The result shows the effectiveness of our method.

Keywords

  • Extractive summarization
  • Sentence extraction
  • Automatic dataset construction
  • Sentence reduction
  • Machine learning

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Correspondence to Satoshi Hiai .

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Hiai, S., Otani, Y., Yamamura, T., Shimada, K. (2019). Automatic Training Data Construction and Extractive Supervised Summarization for NTCIR-14 QA Lab-PoliInfo. In: Kato, M., Liu, Y., Kando, N., Clarke, C. (eds) NII Testbeds and Community for Information Access Research. NTCIR 2019. Lecture Notes in Computer Science(), vol 11966. Springer, Cham. https://doi.org/10.1007/978-3-030-36805-0_8

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  • DOI: https://doi.org/10.1007/978-3-030-36805-0_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-36804-3

  • Online ISBN: 978-3-030-36805-0

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