SOTAVerified

Multi-Document Summarization

Multi-Document Summarization is a process of representing a set of documents with a short piece of text by capturing the relevant information and filtering out the redundant information. Two prominent approaches to Multi-Document Summarization are extractive and abstractive summarization. Extractive summarization systems aim to extract salient snippets, sentences or passages from documents, while abstractive summarization systems aim to concisely paraphrase the content of the documents.

Source: Multi-Document Summarization using Distributed Bag-of-Words Model

Papers

Showing 261270 of 359 papers

TitleStatusHype
Multi-document Summarization Using Bipartite Graphs0
Multi-document summarization using distortion-rate ratio0
Multi-document Summarization using Semantic Role Labeling and Semantic Graph for Indonesian News Article0
Multi-Document Summarization via Discriminative Summary Reranking0
Multi-document Summarization via Deep Learning Techniques: A Survey0
Multi-Document Summarization with Determinantal Point Processes and Contextualized Representations0
Multi-Granularity Interaction Network for Extractive and Abstractive Multi-Document Summarization0
Multi-layered graph-based multi-document summarization model0
MultiLing 2015: Multilingual Summarization of Single and Multi-Documents, On-line Fora, and Call-center Conversations0
Multilingual Multi-Document Summarization with POLY20
Show:102550
← PrevPage 27 of 36Next →

No leaderboard results yet.