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 101110 of 359 papers

TitleStatusHype
Overview of MSLR2022: A Shared Task on Multi-document Summarization for Literature Reviews0
基于实体信息增强及多粒度融合的多文档摘要(Multi-Document Summarization Based on Entity Information Enhancement and Multi-Granularity Fusion)0
An Extractive-Abstractive Approach for Multi-document Summarization of Scientific Articles for Literature ReviewCode0
Document-aware Positional Encoding and Linguistic-guided Encoding for Abstractive Multi-document Summarization0
Parallel Hierarchical Transformer with Attention Alignment for Abstractive Multi-Document Summarization0
Multi-Document Summarization with Centroid-Based PretrainingCode0
ACM -- Attribute Conditioning for Abstractive Multi Document Summarization0
Discriminative Marginalized Probabilistic Neural Method for Multi-Document Summarization of Medical Literature0
Large-Scale Multi-Document Summarization with Information Extraction and Compression0
Predicting Intervention Approval in Clinical Trials through Multi-Document Summarization0
Show:102550
← PrevPage 11 of 36Next →

No leaderboard results yet.