Papers with text editing task
Fact-based Text Editing (2020.acl-main)
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| Challenge: | Existing methods for fact-based text editing are not suitable for all situations. |
| Approach: | They propose a method for automatically generating a dataset with a draft text, revised text, and several facts represented in triples. |
| Outcome: | The proposed method outperforms the encoder-decoder approach on two datasets and shows that it conducts inference faster than the encoded-decoding approach. |
Text Editing by Command (2021.naacl-main)
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| Challenge: | Recent work has focused on making such models more controllable and factually grounded. |
| Approach: | They propose a novel interactive text generation setting in which the user interacts with the system by issuing commands to edit existing text. |
| Outcome: | The proposed model outperforms baseline models and obtains positive results in automatic and human evaluations. |
Encode, Tag, Realize: High-Precision Text Editing (D19-1)
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| Challenge: | Neural sequence-to-sequence models provide a powerful framework for learning to translate source texts into target texts. |
| Approach: | They propose a sequence tagging approach that casts text generation as a text editing task. |
| Outcome: | The proposed model outperforms strong seq2seq models on sentence fusion, sentence splitting, abstractive summarization, and grammar correction tasks and achieves state-of-the-art performance. |