Papers Document Summarization
“Document Summarization” 태그가 달린 논문 804편 · 필터 해제
Strong Drafts Need Compact Memories: Long-Context Speculative Decoding with Compressed KV Cache
Long-context LLM applications such as document summarization and multi-turn agents require generation from prefixes spanning tens of thousands of tokens, making decoding latency a major bottleneck. Speculative decoding (…
Document SummarizationMMLDSum-LLM: Multimodal Long-Document Summarization with Visual-Alignment and Keyword-Aware
Multimodal long documents are core carriers of professional knowledge, where critical evidence is sparsely distributed across paragraphs and modalities. This easily causes key information omission and cross-modal halluci…
Document SummarizationToken-Level Off-Policy Learning for Faithful Generation Under Distribution Shift
We propose Token-Level Off-Policy Labeling (TOPL), an off-policy training paradigm that reframes post-training as a token-level correctness prediction task. Our key intuition is that by training the model to distinguish …
Document SummarizationMachine TranslationA Stepwise Questioning Expert-Editor Multi-Agent Framework for Long-Document Summarization
Although large language models (LLMs) have shown promising potential in news summarization tasks, their performance on long-document summarization remains challenging as their length often exceeds the input limits. As th…
Document SummarizationLess is More: Quality-Aware Training Data Selection for Scientific Summarization
Scientific long-document summarization datasets commonly treat author-written abstracts as gold reference summaries, although their quality and alignment with the source article vary. At the same time, publicly available…
Document SummarizationBeyond the Reranker: Do RAG Retrieval Enhancements Help Once a Strong Reranker Is Present?
Retrieval-augmented generation (RAG) is routinely extended with methods meant to improve retrieval: query expansion, hierarchical and cross-document summarization, graph-based expansion, per-query routing, rank fusion, a…
Document SummarizationSegment-level Tree Search for Long Meeting Document Summarization
Meeting documents are challenging to summarize due to their length and complex conversational structure. Existing approaches typically adopt multi-stage pipelines that extract information prior to summarization; however,…
Document SummarizationMultilingual Coreference Resolution via Cycle-Consistent Machine Translation
Coreference resolution is a core NLP task, having a broad range of downstream applications, e.g.~machine translation, question answering, document summarization, etc. While the task is well-studied in English, comparativ…
Document SummarizationCoreference ResolutionMachine TranslationQuestion AnsweringNyayaAI: An AI-Powered Legal Assistant Using Multi-Agent Architecture and Retrieval-Augmented Generation
Legal information in India remains largely inaccessible due to the complexity of legal language and the sheer volume of legal documentation involved in research and case analysis. This paper presents NyayaAI, an AI-power…
Document SummarizationLongSumEval: Question-Answering Based Evaluation and Feedback-Driven Refinement for Long Document Summarization
Evaluating long document summaries remains the primary bottleneck in summarization research. Existing metrics correlate weakly with human judgments and produce aggregate scores without explaining deficiencies or guiding …
Document SummarizationText GenerationLLM-ReSum: A Framework for LLM Reflective Summarization through Self-Evaluation
Reliable evaluation of large language model (LLM)-generated summaries remains an open challenge, particularly across heterogeneous domains and document lengths. We conduct a comprehensive meta-evaluation of 14 automatic …
Document SummarizationA Multimodal Text- and Graph-Based Approach for Open-Domain Event Extraction from Documents
Event extraction is essential for event understanding and analysis. It supports tasks such as document summarization and decision-making in emergency scenarios. However, existing event extraction approaches have limitati…
Document SummarizationEvent ExtractionDWTSumm: Discrete Wavelet Transform for Document Summarization
Summarizing long, domain-specific documents with large language models (LLMs) remains challenging due to context limitations, information loss, and hallucinations, particularly in clinical and legal settings. We propose …
Document SummarizationSemantic SimilarityCharacterizing Performance-Energy Trade-offs of Large Language Models in Multi-Request Workflows
Large language models (LLMs) are increasingly used in applications forming multi-request workflows like document summarization, search-based copilots, and multi-agent programming. While these workflows unlock richer func…
Document SummarizationDECKBench: Benchmarking Multi-Agent Frameworks for Academic Slide Generation and Editing
Automatically generating and iteratively editing academic slide decks requires more than document summarization. It demands faithful content selection, coherent slide organization, layout-aware rendering, and robust mult…
Document SummarizationInstruction FollowingAdvantages of Domain Knowledge Injection for Legal Document Summarization: A Case Study on Summarizing Indian Court Judgments in English and Hindi
Summarizing Indian legal court judgments is a complex task not only due to the intricate language and unstructured nature of the legal texts, but also since a large section of the Indian population does not understand th…
Document SummarizationSupporting software engineering tasks with agentic AI: Demonstration on document retrieval and test scenario generation
The introduction of large language models ignited great retooling and rethinking of the software development models. The ensuing response of software engineering research yielded a massive body of tools and approaches. I…
Document SummarizationQuestion AnsweringFrom Global to Local: Learning Context-Aware Graph Representations for Document Classification and Summarization
Recent NLP systems commonly represent documents as linear token sequences. Although this captures sequential order, it can hinder modeling long-range dependencies and global document structure, especially for long texts.…
Document ClassificationDocument SummarizationLarge Language Models: A Mathematical Formulation
Large language models (LLMs) process and predict sequences containing text to answer questions, and address tasks including document summarization, providing recommendations, writing software and solving quantitative pro…
Document SummarizationDisco-RAG: Discourse-Aware Retrieval-Augmented Generation
Retrieval-Augmented Generation (RAG) has emerged as an important means of enhancing the performance of large language models (LLMs) in knowledge-intensive tasks. However, most existing RAG strategies treat retrieved pass…
Document SummarizationQuestion Answering