arXiv:1405.7975
Multi-layered graph-based multi-document summarization model
원문 초록
Multi-document summarization is a process of automatic generation of a compressed version of the given collection of documents. Recently, the graph-based models and ranking algorithms have been actively investigated by the extractive document summarization community. While most work to date focuses on homogeneous connecteness of sentences and heterogeneous connecteness of documents and sentences (e.g. sentence similarity weighted by document importance), in this paper we present a novel 3-layered graph model that emphasizes not only sentence and document level relations but also the influence of under sentence level relations (e.g. a part of sentence similarity).
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Extractive Multi-document Summarization using K-means, Centroid-based Method, MMR, and Sentence Position
GenerationPrograms: Fine-grained Attribution with Executable Programs
Improving Fairness of Large Language Models in Multi-document Summarization
Ask, Retrieve, Summarize: A Modular Pipeline for Scientific Literature Summarization
Estimating Optimal Context Length for Hybrid Retrieval-augmented Multi-document Summarization
A Unified Retrieval Framework with Document Ranking and EDU Filtering for Multi-document Summarization
토론
이 논문과 연결된 포럼 글이 아직 없습니다.