Papers with machine-generated summaries
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. |
Sentence Mover’s Similarity: Automatic Evaluation for Multi-Sentence Texts (P19-1)
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| Challenge: | Existing automatic metrics for evaluating text are expensive and time-consuming. |
| Approach: | They propose automatic metrics that evaluate text in a continuous space using word and sentence embeddings. |
| Outcome: | The proposed method outperforms ROUGE on machine-generated summaries and human-authored essays on human-generated texts. |
Interpretable Multi-headed Attention for Abstractive Summarization at Controllable Lengths (2020.coling-main)
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| Challenge: | Abstractive summarization at controllable lengths is a challenging task in natural language processing . high variance in screen-sizes often require extensive human supervision to perform these modifications. |
| Approach: | They propose a supervised method to construct abstractive summaries of a text document at controllable lengths using an interpretable multi-headed attention mechanism. |
| Outcome: | The proposed method outperforms baselines on two low-resource datasets in English by 14.70%. |