Papers with extractive and abstractive summarization models
Liputan6: A Large-scale Indonesian Dataset for Text Summarization (2020.aacl-main)
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| Challenge: | Despite having the fourth largest speaker population in the world, 1 Indonesian is under-represented in NLP. |
| Approach: | They propose to use a large-scale Indonesian summarization dataset to test extractive and abstractive summarizing methods. |
| Outcome: | The proposed methods are compared with multilingual and monolingual BERT-based models. |
BOOKSUM: A Collection of Datasets for Long-form Narrative Summarization (2022.findings-emnlp)
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| Challenge: | Existing text summarization datasets include short-form source documents that lack long-range causal and temporal dependencies and contain strong layout and stylistic biases. |
| Approach: | They propose a dataset for long-form narrative summarization that uses human written summaries on three levels of difficulty. |
| Outcome: | The proposed dataset covers documents from the literature domain, such as novels, plays and stories, and includes highly abstractive, human written summaries on three levels of difficulty. |