Papers with automatically generating
Improving Absent Keyphrase Generation with Diversity Heads (2024.findings-naacl)
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| Challenge: | Existing approaches to generating keyphrases for a given text are limited to extracting only the keyphrase that is directly seen in the document. |
| Approach: | They propose to treat present keyphrase extraction as a sequence labeling problem and treat absent keyphrases together in a text-to-text generation framework during training. |
| Outcome: | The proposed model improves on the state-of-the-art for present keyphrase extraction and five datasets for absent keyphrase generation among the six English datasets. |
Zero-Shot Keyphrase Generation: Investigating Specialized Instructions and Multi-sample Aggregation on Large Language Models (2025.findings-naacl)
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| Challenge: | Keyphrase generation is a long-standing NLP task for automatically generating keyphrases for a given document. |
| Approach: | They propose to use open-source instruction-tuned LLMs for keyphrase generation . they propose task-specific counterparts to self-consistency-style strategies for LLM . |
| Outcome: | The proposed model improves on existing models and shows significant benefits over baselines. |
Open-domain Video Commentary Generation (2022.emnlp-main)
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Edison Marrese-Taylor, Yumi Hamazono, Tatsuya Ishigaki, Goran Topić, Yusuke Miyao, Ichiro Kobayashi, Hiroya Takamura
| Challenge: | Existing approaches to generate live commentary on specific domains have been limited. |
| Approach: | They propose to generate live commentary from transcribed videos in an open-domain setting . they propose to use well-known neural architectures to build models based on transcriptions . |
| Outcome: | The proposed model is based on well-known neural architectures and based off existing models. |
GlossLM: A Massively Multilingual Corpus and Pretrained Model for Interlinear Glossed Text (2024.emnlp-main)
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| Challenge: | Existing resources for standardized, easily accessible IGT data limit their applicability to linguistic research. |
| Approach: | They compile the largest existing corpus of interlinear glossed text data from a variety of sources and use it to generate annotated text. |
| Outcome: | The proposed model outperforms SOTA models on monolingual corpora by 6.6%. |
README: Bridging Medical Jargon and Lay Understanding for Patient Education through Data-Centric NLP (2024.findings-emnlp)
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Zonghai Yao, Nandyala Siddharth Kantu, Guanghao Wei, Hieu Tran, Zhangqi Duan, Sunjae Kwon, Zhichao Yang, Hong Yu
| Challenge: | a new task is to generate lay definitions of medical terms in EHRs that are difficult to understand for patients. |
| Approach: | They propose a task of automatically generating lay definitions to simplify medical terms into patient-friendly lay language. |
| Outcome: | The proposed model can match or surpass state-of-the-art closed-source large language models like ChatGPT with high-quality data. |