Papers Multi-Document Summarization
“Multi-Document Summarization” 태그가 달린 논문 374편 · 필터 해제
Decoupling Generation and Selection for Budget-Constrained Faithful Summarization
Abstractive summarization models remain vulnerable to factual inconsistency, redundancy, and weak length control. We propose a modular generation-and-selection framework for sentence-budget-constrained summarization. A p…
Multi-Document SummarizationAttributable by Construction: Claim-Anchored Provenance for Multi-Document Summarization
Large language models produce fluent multi-document summaries, but their attributions are typically coarse---whole documents or passages---and generated post hoc, leaving each statement hard to verify. We argue that attr…
Multi-Document SummarizationA BART-based approach with hierarchical strategy for Vietnamese abstractive multi-document summarization
In this technical report, we focus on solving the challenge of Vietnamese multi-document abstractive summarization, introduced in the International Workshop on Vietnamese Language and Speech Processing (VLSP) 2022. We ch…
Multi-Document SummarizationDetecting Speculative Language in Biomedical Texts using Recurrent Neural Tensor Networks
In this investigation, we delve into the automated detection of speculative language within biomedical articles by utilizing distributed sentence representations and advanced deep learning techniques. The implications of…
Multi-Document SummarizationInformation RetrievalA Training-Free Mixture-of-Agents Framework for Multi-Document Summarization using LLMs and Knowledge Graphs
Multi-Document Summarization (MDS) plays a critical role in distilling essential information from collections of textual data. Existing approaches often struggle to capture complex inter-document relationships, rely heav…
Multi-Document SummarizationKnowledge GraphsA Dataset and Benchmark for Consumer Healthcare Question Summarization
The quest for seeking health information has swamped the web with consumers health-related questions. Generally, consumers use overly descriptive and peripheral information to express their medical condition or other hea…
Natural Language UnderstandingCommunity Question AnsweringMulti-Document SummarizationNarrative Consolidation: Formulating a New Task for Unifying Multi-Perspective Accounts
Processing overlapping narrative documents, such as legal testimonies or historical accounts, often aims not for compression but for a unified, coherent, and chronologically sound text. Standard Multi-Document Summarizat…
Multi-Document SummarizationInput Order Shapes LLM Semantic Alignment in Multi-Document Summarization
Large language models (LLMs) are now used in settings such as Google's AI Overviews, where it summarizes multiple long documents. However, it remains unclear whether they weight all inputs equally. Focusing on abortion-r…
Multi-Document SummarizationSemantic SimilarityLeveraging Digitized Newspapers to Collect Summarization Data in Low-Resource Languages
High quality summarization data remains scarce in under-represented languages. However, historical newspapers, made available through recent digitization efforts, offer an abundant source of untapped, naturally annotated…
Multi-Document SummarizationLeveraging Hierarchical Organization for Medical Multi-document Summarization
Medical multi-document summarization (MDS) is a complex task that requires effectively managing cross-document relationships. This paper investigates whether incorporating hierarchical structures in the inputs of MDS can…
Multi-Document SummarizationSemShareKV: Efficient KVCache Sharing for Semantically Similar Prompts via Token-Level LSH Matching
As large language models (LLMs) continue to scale, the memory footprint of key-value (KV) caches during inference has become a significant bottleneck. Existing approaches primarily focus on compressing KV caches within a…
Multi-Document SummarizationComparative Personalization for Multi-document Summarization
Personalized multi-document summarization (MDS) is essential for meeting individual user preferences of writing style and content focus for summaries. In this paper, we propose that for effective personalization, it is i…
Multi-Document SummarizationTopic-Guided Reinforcement Learning with LLMs for Enhancing Multi-Document Summarization
A key challenge in Multi-Document Summarization (MDS) is effectively integrating information from multiple sources while maintaining coherence and topical relevance. While Large Language Models have shown impressive resu…
Multi-Document SummarizationReinforcement LearningAccelerating Scientific Discovery with Multi-Document Summarization of Impact-Ranked Papers
The growing volume of scientific literature makes it challenging for scientists to move from a list of papers to a synthesized understanding of a topic. Because of the constant influx of new papers on a daily basis, even…
Multi-Document SummarizationMRGSEM-Sum: An Unsupervised Multi-document Summarization Framework based on Multi-Relational Graphs and Structural Entropy Minimization
The core challenge faced by multi-document summarization is the complexity of relationships among documents and the presence of information redundancy. Graph clustering is an effective paradigm for addressing this issue,…
Multi-Document SummarizationGraph ClusteringGenerationPrograms: Fine-grained Attribution with Executable Programs
Recent large language models (LLMs) achieve impressive performance in source-conditioned text generation but often fail to correctly provide fine-grained attributions for their outputs, undermining verifiability and trus…
Document SummarizationLong Form Question AnsweringMulti-Document SummarizationQuestion Answering+1Improving Fairness of Large Language Models in Multi-document Summarization
Fairness in multi-document summarization (MDS) is crucial for providing comprehensive views across documents with diverse social attribute values, which can significantly impact decision-making. For example, a summarizat…
AttributeDecision MakingDocument SummarizationFairness+1Ask, Retrieve, Summarize: A Modular Pipeline for Scientific Literature Summarization
The exponential growth of scientific publications has made it increasingly difficult for researchers to stay updated and synthesize knowledge effectively. This paper presents XSum, a modular pipeline for multi-document s…
Document SummarizationMulti-Document SummarizationQuestion GenerationQuestion-Generation+3A Unified Retrieval Framework with Document Ranking and EDU Filtering for Multi-document Summarization
In the field of multi-document summarization (MDS), transformer-based models have demonstrated remarkable success, yet they suffer an input length limitation. Current methods apply truncation after the retrieval process …
Document RankingDocument SummarizationMulti-Document SummarizationRetrievalEstimating Optimal Context Length for Hybrid Retrieval-augmented Multi-document Summarization
Recent advances in long-context reasoning abilities of language models led to interesting applications in large-scale multi-document summarization. However, prior work has shown that these long-context models are not eff…
Document SummarizationMulti-Document SummarizationRAGRetrieval