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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2309.07990 |
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| _version_ | 1866914738712084480 |
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| author | Bhowmik, Rajarshi Ponza, Marco Tendle, Atharva Gupta, Anant Jiang, Rebecca Lu, Xingyu Zhao, Qian Preotiuc-Pietro, Daniel |
| author_facet | Bhowmik, Rajarshi Ponza, Marco Tendle, Atharva Gupta, Anant Jiang, Rebecca Lu, Xingyu Zhao, Qian Preotiuc-Pietro, Daniel |
| contents | In text documents such as news articles, the content and key events usually revolve around a subset of all the entities mentioned in a document. These entities, often deemed as salient entities, provide useful cues of the aboutness of a document to a reader. Identifying the salience of entities was found helpful in several downstream applications such as search, ranking, and entity-centric summarization, among others. Prior work on salient entity detection mainly focused on machine learning models that require heavy feature engineering. We show that fine-tuning medium-sized language models with a cross-encoder style architecture yields substantial performance gains over feature engineering approaches. To this end, we conduct a comprehensive benchmarking of four publicly available datasets using models representative of the medium-sized pre-trained language model family. Additionally, we show that zero-shot prompting of instruction-tuned language models yields inferior results, indicating the task's uniqueness and complexity. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_07990 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Leveraging Contextual Information for Effective Entity Salience Detection Bhowmik, Rajarshi Ponza, Marco Tendle, Atharva Gupta, Anant Jiang, Rebecca Lu, Xingyu Zhao, Qian Preotiuc-Pietro, Daniel Computation and Language In text documents such as news articles, the content and key events usually revolve around a subset of all the entities mentioned in a document. These entities, often deemed as salient entities, provide useful cues of the aboutness of a document to a reader. Identifying the salience of entities was found helpful in several downstream applications such as search, ranking, and entity-centric summarization, among others. Prior work on salient entity detection mainly focused on machine learning models that require heavy feature engineering. We show that fine-tuning medium-sized language models with a cross-encoder style architecture yields substantial performance gains over feature engineering approaches. To this end, we conduct a comprehensive benchmarking of four publicly available datasets using models representative of the medium-sized pre-trained language model family. Additionally, we show that zero-shot prompting of instruction-tuned language models yields inferior results, indicating the task's uniqueness and complexity. |
| title | Leveraging Contextual Information for Effective Entity Salience Detection |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2309.07990 |