Simple is not Enough: Document-level Text Simplification using Readability and Coherence
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866910762310565888 |
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| author | Vásquez-Rodríguez, Laura Nguyen, Nhung T. H. Przybyła, Piotr Shardlow, Matthew Ananiadou, Sophia |
| author_facet | Vásquez-Rodríguez, Laura Nguyen, Nhung T. H. Przybyła, Piotr Shardlow, Matthew Ananiadou, Sophia |
| contents | In this paper, we present the SimDoc system, a simplification model considering simplicity, readability, and discourse aspects, such as coherence. In the past decade, the progress of the Text Simplification (TS) field has been mostly shown at a sentence level, rather than considering paragraphs or documents, a setting from which most TS audiences would benefit. We propose a simplification system that is initially fine-tuned with professionally created corpora. Further, we include multiple objectives during training, considering simplicity, readability, and coherence altogether. Our contributions include the extension of professionally annotated simplification corpora by the association of existing annotations into (complex text, simple text, readability label) triples to benefit from readability during training. Also, we present a comparative analysis in which we evaluate our proposed models in a zero-shot, few-shot, and fine-tuning setting using document-level TS corpora, demonstrating novel methods for simplification. Finally, we show a detailed analysis of outputs, highlighting the difficulties of simplification at a document level. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2412_18655 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Simple is not Enough: Document-level Text Simplification using Readability and Coherence Vásquez-Rodríguez, Laura Nguyen, Nhung T. H. Przybyła, Piotr Shardlow, Matthew Ananiadou, Sophia Computation and Language In this paper, we present the SimDoc system, a simplification model considering simplicity, readability, and discourse aspects, such as coherence. In the past decade, the progress of the Text Simplification (TS) field has been mostly shown at a sentence level, rather than considering paragraphs or documents, a setting from which most TS audiences would benefit. We propose a simplification system that is initially fine-tuned with professionally created corpora. Further, we include multiple objectives during training, considering simplicity, readability, and coherence altogether. Our contributions include the extension of professionally annotated simplification corpora by the association of existing annotations into (complex text, simple text, readability label) triples to benefit from readability during training. Also, we present a comparative analysis in which we evaluate our proposed models in a zero-shot, few-shot, and fine-tuning setting using document-level TS corpora, demonstrating novel methods for simplification. Finally, we show a detailed analysis of outputs, highlighting the difficulties of simplification at a document level. |
| title | Simple is not Enough: Document-level Text Simplification using Readability and Coherence |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2412.18655 |