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 7180 of 359 papers

TitleStatusHype
UPER: Boosting Multi-Document Summarization with an Unsupervised Prompt-based ExtractorCode0
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
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
Multi-LexSum: Real-World Summaries of Civil Rights Lawsuits at Multiple GranularitiesCode1
ACM -- Attribute Conditioning for Abstractive Multi Document Summarization0
Improving Multi-Document Summarization through Referenced Flexible Extraction with Credit-AwarenessCode1
Discriminative Marginalized Probabilistic Neural Method for Multi-Document Summarization of Medical Literature0
Large-Scale Multi-Document Summarization with Information Extraction and Compression0
Show:102550
← PrevPage 8 of 36Next →

No leaderboard results yet.