| Challenge: | Existing literature reviews focus on summarizing individual papers without addressing the need for expository and transition sentences to explain the relationships among multiple papers. |
| Approach: | They propose a feature-based, LLM-prompting approach to generate richer citation texts . they propose to use related work sections of scientific articles as proxy for the kind of short, customized, daily feed summaries . |
| Outcome: | The proposed approach captures complex relationships among multiple papers while generating richer citation texts. |
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Automatic Generation of Citation Texts in Scholarly Papers: A Pilot Study (2020.acl-main)
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| Challenge: | Existing studies on automatic generation of citation texts in scholarly papers have not investigated this problem. |
| Approach: | They propose to train an implicit citation extraction model based on BERT and a multi-source pointer-generator network with cross attention mechanism for citation text generation. |
| Outcome: | The proposed model can generate short texts to describe cited papers in scholarly papers with training data. |
Related Work and Citation Text Generation: A Survey (2024.emnlp-main)
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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 . |
| Approach: | They propose to use automatic related work generation (RWG) to generate papers . authors summarize key approaches and define tasks in a zoo of historical works . |
| Outcome: | a new study summarises key approaches and defines the tasks and discusses the challenges of RWG. |
A Multi-level Annotated Corpus of Scientific Papers for Scientific Document Summarization and Cross-document Relation Discovery (2020.lrec-1)
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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. |
| Outcome: | The proposed corpus expands the existing data-set of related work sections and cites the papers cited in the related work section. |
Bridging Internal Consistency and External Alignment: A Causal and Dynamic Interpretability Framework for LLM Generation (2026.acl-long)
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| Challenge: | Existing interpretability methods focus on internal and external aspects of the model . existing explanations often focus on surface correlations or static dependencies . |
| Approach: | They propose a causal and dynamic interpretability framework for Large Language Models . they characterize backdoor-adjusted causal effects of generated prefix and prompt . |
| Outcome: | The proposed framework provides a unified causal view of internal consistency and external alignment in LLM generation dynamics. |
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. |
Bringing Structure into Summaries: a Faceted Summarization Dataset for Long Scientific Documents (2021.acl-short)
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| Challenge: | Faceted summarization provides briefings of a document from different perspectives. |
| Approach: | They propose a faceted summarization benchmark built on Emerald journal articles . they propose faceted models that bring structure into faceted documents . |
| Outcome: | The proposed benchmark is based on Emerald journal articles and covers a diverse range of domains. |
Can LLMs Help Uncover Insights about LLMs? A Large-Scale, Evolving Literature Analysis of Frontier LLMs (2025.acl-long)
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| Challenge: | Recent surveys of literature highlight the overwhelming growth of Large Language Models (LLMs). |
| Approach: | They propose a semi-automated literature analysis approach that automates literature analysis using LLMs. |
| Outcome: | The proposed approach reduces paper surveying and data extraction by 93% compared to manual methods. |
Relational Summarization for Corpus Analysis (N18-1)
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| Challenge: | Existing methods for summarizing textual content are often ignored . relationshipal questions are ubiquitous and varied. |
| Approach: | They propose a method which generates a natural language summary of the relationship between two lexical items in a corpus without reference to a knowledge base. |
| Outcome: | The proposed method generates a natural language summary of the relationship between two lexical items in a corpus without reference to a knowledge base. |
Citance-Contextualized Summarization of Scientific Papers (2023.findings-emnlp)
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| Challenge: | Current automatic summarization approaches generate abstracts, but abstracts do not show relationship between paper and references. |
| Approach: | They propose a contextualized summarization approach that generates an informative summary . they extract and model the citances of a paper, retrieve relevant passages from cited papers, and generate abstractive summaries tailored to each citance. |
| Outcome: | The proposed method extracts and models the citances of a paper, retrieves relevant passages from cited papers, and generates abstractive summaries tailored to each citance. |
CitationIE: Leveraging the Citation Graph for Scientific Information Extraction (2021.acl-long)
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| Challenge: | Existing work on scientific information extraction (SciIE) considers extraction solely based on the content of an individual paper, without considering the paper’s place in the broader literature. |
| Approach: | They propose to automate the extraction of key information from scientific documents by leveraging a complementary source: the citation graph of referential links between citing and cited papers. |
| Outcome: | The proposed model improves on a set of English-language scientific documents. |