Pre-training Meets Clustering: A Hybrid Extractive Multi-document Summarization Model
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-27409-1_48
Reference21 articles.
1. Rezaei, A., Dami, S., Daneshjoo, P.: Multi-document extractive text summarization via deep learning approach. In: 2019 5th Conference on Knowledge Based Engineering and Innovation (KBEI), pp. 680–685. IEEE (2019)
2. Mallick, R., Susan, S., Agrawal, V., Garg, R., Rawal, P.: Context-and sequence-aware convolutional recurrent encoder for neural machine translation. In: Proceedings of the 36th Annual ACM Symposium on Applied Computing, pp. 853–856 (2021)
3. Tsoumou, E.S.L., Lai, L., Yang, S., Varus, M.L.: An extractive multi-document summarization technique based on fuzzy logic approach. In: 2016 International Conference on Network and Information Systems for Computers (ICNISC), pp. 346–351. IEEE (2016)
4. Yapinus, G., Erwin, A., Galinium, M., Muliady, W.: Automatic multi-document summarization for Indonesian documents using hybrid abstractive-extractive summarization technique. In: 2014 6th International Conference on Information Technology and Electrical Engineering (ICITEE), pp. 1–5. IEEE (2014)
5. Hirao, T., Fukusima, T., Okumura, M., Nobata, C., Nanba, H.: Corpus and evaluation measures for multiple document summarization with multiple sources. In: Proceedings of the Twentieth International Conference on Computational Linguistics (COLING), pp. 535–541 (2004)
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