Fixed length summarization aims at generating summaries with a preset number of words or characters. Most recent researches incorporate length information with word embeddings as the input to the recurrent decoding unit, causing a compromise between length controllability and summary quality. In this work, we present an effective length controlling unit Length Attention (LenAtten) to break this trade-off. Experimental results show that LenAtten not only brings improvements in length controllability and ROGUE scores but also has great generalization ability. In the task of generating a summary with the target length, our model is 732 times better than the best-performing length controllable summarizer in length controllability on the CNN/Daily Mail dataset.
CITATION STYLE
Yu, Z., Wu, Z., Zheng, H., Zhe, X. Y., Fong, J., & Su, W. (2021). LenAtten: An Effective Length Controlling Unit For Text Summarization. In Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (pp. 363–370). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2021.findings-acl.31
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