Document Summarization
7개 벤치마크 · 논문 804편 · 이 태스크의 논문 보기 →
Benchmarks
CNN / Daily Mail
HowSumm-Step
HowSumm-Method
BBC XSum
WikiLingua (tr->en)
Most implemented
Get To The Point: Summarization with Pointer-Generator Networks
Language Models are Unsupervised Multitask Learners
Text Summarization with Pretrained Encoders
Unified Language Model Pre-training for Natural Language Understanding and Generation
GLM: General Language Model Pretraining with Autoregressive Blank Infilling
Papers
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 Summarization