The Influence of Data Pre-processing and Post-processing on Long Document Summarization
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Abstract (arXiv)
Long document summarization is an important and hard task in the field of natural language processing. A good performance of the long document summarization reveals the model has a decent understanding of the human language. Currently, most researches focus on how to modify the attention mechanism of the transformer to achieve a higher ROUGE score. The study of data pre-processing and post-processing are relatively few. In this paper, we use two pre-processing methods and a post-processing method and analyze the effect of these methods on various long document summarization models.
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References · 7
- 2017Attention Is All You Needcited by 60,361
- 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
- 2020Longformer: The Long-Document Transformercited by 3,413
- 2020Big Bird: Transformers for Longer Sequencescited by 1,684
- 2018A Discourse-Aware Attention Model for Abstractive Summarization of Long Documentscited by 478
- 2021Efficient Attentions for Long Document Summarizationcited by 345
- 2019On Extractive and Abstractive Neural Document Summarization with Transformer Language Modelscited by 83
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