Capturing Relations between Scientific Papers: An Abstractive Model for Related Work Section Generation (2021.acl-long)
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| Challenge: | Existing related work generation models are inflexible and extract sentences from multiple papers to form a related work discussion. |
| Approach: | They propose a Relation-aware Related work generator which generates an abstractive related work from the given multiple scientific papers in the same research area. |
| Outcome: | The proposed model improves over existing models and can be used to familiarize researchers with the state of the art in the field. |
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| Challenge: | Existing models ignore the inherent causality during related work generation, leading to spurious correlations which downgrade the models’ generation quality and generalizability. |
| Approach: | They propose a Causal Intervention Module for Related Work Generation (CaM) that captures causal relationships in related work generation and implements causal interventions to mitigate the negative impact of spurious correlations. |
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| Challenge: | Recent studies have proposed to take advantage of the scientific paper's citation network to approach literature summarization. |
| Approach: | They propose to annotate related work sections, cite papers and sentences using machine readable data and an additional layer of papers citing the references. |
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CORWA: A Citation-Oriented Related Work Annotation Dataset (2022.naacl-main)
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| Challenge: | Academic research is an exploratory activity to discover new solutions to problems . prior work focused on the sentence as the basic unit of generation, neglecting that related work sections consist of variable length text fragments derived from different information sources. |
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Towards a Unified Framework for Reference Retrieval and Related Work Generation (2023.findings-emnlp)
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| Challenge: | Existing methods for related work generation use human-annotated references as information sources. |
| Approach: | They propose a model which combines reference retrieval and related work generation processes in a unified framework based on the large language model. |
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| Challenge: | Academic research paper authors must perform literature review to compare work with prior work . authors must compose coherent story that connects prior work and current work based on author's understanding of field . |
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Related Work Is All You Need (2024.lrec-main)
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| Challenge: | a corpus of 400k annotations of related work is used to generate a "related work" section . authors and researchers often turn to tools like Google Scholar to find related research for their papers . |
| Approach: | They propose to use a corpus with 400k annotations to generate a "related work" section . they propose to automate the process by using a newly-released corpus that contains human annotations . |
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Dataset Construction for Scientific-Document Writing Support by Extracting Related Work Section and Citations from PDF Papers (2022.lrec-1)
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| Challenge: | To augment datasets used for scientific-document writing support research, we extract texts from “Related Work” sections and citation information in PDF-formatted papers published in English. |
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SciXGen: A Scientific Paper Dataset for Context-Aware Text Generation (2021.findings-emnlp)
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A Novel Table-to-Graph Generation Approach for Document-Level Joint Entity and Relation Extraction (2023.acl-long)
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| Challenge: | Existing document-level relation extraction methods assume entities and their mentions are given beforehand, which is inadequate for real-world applications. |
| Approach: | They propose a table-to-graph generation model for joint extraction of entities and relations at document-level. |
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Multi-XScience: A Large-scale Dataset for Extreme Multi-document Summarization of Scientific Articles (2020.emnlp-main)
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| Challenge: | Multi-XScience is a dataset construction protocol that favours abstractive modeling approaches. |
| Approach: | They propose a large-scale multi-document summarization dataset that is based on articles and lexical databases and WordNet synonymy information to generate related-work sections of a paper. |
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