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.
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Hiai, S., Otani, Y., Yamamura, T., Shimada, K.: KitAi-PI: summarization system for NTCIR-14 QA Lab-Poliinfo. In: Proceedings of The 14th NTCIR Conference on Evaluation of Information Access Technologies (2019)
Katragadda, R., Pingali, P., Varma, V.: Sentence position revisited: a robust light-weight update summarization baseline algorithm. In: Proceedings of the Third International Workshop on Cross Lingual Information Access: Addressing the Information Need of Multilingual Societies (CLIAWS3), pp. 46–52 (2009)
Kimura, T., Tagami, R., Katsuyama, H., Sugimoto, S., Miyamori, H.: KSU systems at the NTCIR-14 QA Lab-Poliinfo task. In: Proceedings of The 14th NTCIR Conference on Evaluation of Information Access Technologies (2019)
Kimura, Y., et al.: Overview of the NTCIR-14 QA Lab-Poliinfo task. In: Proceedings of the 14th NTCIR Conference on Evaluation of Information Access Technologies (2019)
Kiyota, Y., Kurohashi, S.: Automatic summarization of Japanese sentences and its application to a WWW KWIC index. In: Proceedings of 2001 Symposium on Applications and the Internet (2001)
Kubo, T.: MeCab: yet another part-of-speech and morphological analyzer. http://mecab.sourceforge.net/
Lin, C.Y.: Rouge: a package for automatic evaluation of summaries. In: Proceedings of the Eighth Workshop on the Annual Meeting of the Association for Computational Linguistics (ACL-04), pp. 74–81 (2004)
Mihalcea, R., Tarau, P.: TextRank: bringing order into text. In: Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing (2004)
Mikami, M., Masuyama, S., Nakagawa, S.: A summarization method by reducing redundancy of each sentence for making captions of newscasting. J. Nat. Lang. Process. 6(6), 65–91 (1999)
Ogawa, Y., Satou, M., Komamizu, T., Toyama, K.: nagoy team’s summarization system at the NTCIR-14 QA Lab-Poliinfo. In: Proceedings of The 14th NTCIR Conference on Evaluation of Information Access Technologies (2019)
Ouyang, Y., Li, W., Lu, Q., Zhang, R.: A study on position information in document summarization. In: Proceedings of the 23rd International Conference on Computational, pp. 919–927 (2010)
Shinjo, T., Nishikawa, H., Tokunaga, T.: TTECH at the NTCIR-14 QA Lab-Poliinfo task. In: Proceedings of The 14th NTCIR Conference on Evaluation of Information Access Technologies (2019)
Tang, L., Watanabe, K., Yada, S., Kageura, K.: LisLb-Team at the NTCIR-14 QA Lab-Poliinfo task. In: Proceedings of The 14th NTCIR Conference on Evaluation of Information Access Technologies (2019)
Terazawa, K., Shirato, D., Akiba, T., Masuyama, S.: AKBL at the NTCIR-14 QA Lab-Poliinfo task. In: Proceedings of The 14th NTCIR Conference on Evaluation of Information Access Technologies (2019)
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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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