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 351359 of 359 papers

TitleStatusHype
Improving the Similarity Measure of Determinantal Point Processes for Extractive Multi-Document SummarizationCode0
Scoring Sentence Singletons and Pairs for Abstractive SummarizationCode0
Ask, Retrieve, Summarize: A Modular Pipeline for Scientific Literature SummarizationCode0
Multi-News: a Large-Scale Multi-Document Summarization Dataset and Abstractive Hierarchical ModelCode0
Multi-News+: Cost-efficient Dataset Cleansing via LLM-based Data AnnotationCode0
QA-Align: Representing Cross-Text Content Overlap by Aligning Question-Answer PropositionsCode0
An Extractive-Abstractive Approach for Multi-document Summarization of Scientific Articles for Literature ReviewCode0
Bridging the gap between extractive and abstractive summaries: Creation and evaluation of coherent extracts from heterogeneous sourcesCode0
Using Query Expansion in Manifold Ranking for Query-Oriented Multi-Document SummarizationCode0
Show:102550
← PrevPage 15 of 15Next →

No leaderboard results yet.