Papers with abstractive and extractive models
Generating Multiple-Length Summaries via Reinforcement Learning for Unsupervised Sentence Summarization (2022.findings-emnlp)
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| Challenge: | Existing models to summarize texts without ground-truth summaries are extractive, which remove words from texts and thus are less flexible than abstractive models. |
| Approach: | They propose an unsupervised model that extracts words from texts and makes them mutually enhance each other. |
| Outcome: | The proposed model outperforms both abstractive and extractive models, while generating new words not contained in input texts. |
GameWikiSum: a Novel Large Multi-Document Summarization Dataset (2020.lrec-1)
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| Challenge: | Existing datasets contain only hundreds of samples, resulting in heavy reliance on hand-crafted features or manually annotated data. |
| Approach: | They propose a new domain-specific dataset for multi-document summarization that is 100 times larger than commonly used datasets. |
| Outcome: | The proposed dataset is 100 times larger than commonly used datasets and in another domain than news. |