Improving Human Text Comprehension through Semi-Markov CRF-based Neural Section Title Generation (N19-1)
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| Challenge: | a study of long documents that do not include short sections in their titles shows that they improve comprehension and speed . |
| Approach: | They propose to extract the most salient sentence and apply deletion-based compression to generate section titles in low-resource environments. |
| Outcome: | The proposed approach outperforms other methods in low-resource environments while outperforming other approaches. |
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| Challenge: | Existing methods for sequence labeling tasks such as Named Entity Recognition (NER) suffer from quadratic complexity over sequence length and poor performance compared to CRF. |
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| Challenge: | Formal documents are organized into sections of text, each with a title . but there is no corpus of web documents annotated with titles and prose texts . cnn.com's john mccarthy and daniel mclears are working on a new title-text dataset . |
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Supervised and Unsupervised Methods for Robust Separation of Section Titles and Prose Text in Web Documents (D18-1)
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| Challenge: | a web text structure is underutilized, but its visual organization is useful for NLP tasks . a flexible system for extracting hierarchical section titles and prose organization is developed . |
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A Neural CRF-based Hierarchical Approach for Linear Text Segmentation (2023.findings-eacl)
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Inderjeet Nair, Aparna Garimella, Balaji Vasan Srinivasan, Natwar Modani, Niyati Chhaya, Srikrishna Karanam, Sumit Shekhar
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| Challenge: | Experimental results show that our proposed model outperforms all previous approaches for monolingual word alignment. |
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Re3val: Reinforced and Reranked Generative Retrieval (2024.findings-eacl)
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| Challenge: | generative retrieval models encode pointers to information in a corpus as an index within the model’s parameters. |
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