Papers by Ahmed Elsayed
Fine-grained Information Extraction from Biomedical Literature based on Knowledge-enriched Abstract Meaning Representation (2021.acl-long)
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| Challenge: | Compared with general natural language texts, sentences from scientific papers usually possess wider contexts between knowledge elements. |
| Approach: | They propose a novel biomedical Information Extraction model to extract scientific entities and events from English research papers using Abstract Meaning Representation (AMR) they construct a sentence-level knowledge graph from an external knowledge base and encode it to improve the model's understanding of complex scientific concepts. |
| Outcome: | The proposed model can extract scientific entities and events from scientific literature and improve its understanding of complex scientific concepts. |
Improving Factuality in Clinical Abstractive Multi-Document Summarization by Guided Continued Pre-training (2024.naacl-short)
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| Challenge: | Existing methods for enhancing the factual accuracy of abstractive summarization models are not effective in fact-critical domains like clinical document summarizing. |
| Approach: | They propose a guided continued pre-training stage for encoder-decoder models followed by supervised fine-tuning on summarization. |
| Outcome: | The proposed approach improves the quality and factuality of the summaries and achieves the best-published results on the clinical document summarization task. |