Reinforced Abstractive Text Summarization With Semantic Added Reward
Text summarization is an important task in natural language processing (NLP). Neural summary models summarize information by understanding and rewriting documents through the encoder-decoder structure. Recent studies have sought to overcome the bias that cross-entropy-based learning methods can have...
保存先:
| 主要な著者: | , |
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| フォーマット: | Artigo |
| 言語: | Inglês |
| 出版事項: |
IEEE
2021-01-01
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| シリーズ: | IEEE Access |
| 主題: | |
| オンライン･アクセス: | https://ieeexplore.ieee.org/document/9483920/ |
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