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 131140 of 359 papers

TitleStatusHype
Multi-document Summarization using Semantic Role Labeling and Semantic Graph for Indonesian News Article0
Data Augmentation for Abstractive Query-Focused Multi-Document SummarizationCode1
ELSKE: Efficient Large-Scale Keyphrase ExtractionCode0
A novel extractive multi-document text summarization system using quantum-inspired genetic algorithm: MTSQIGA0
Neural Abstractive Multi-Document Summarization: Hierarchical or Flat Structure?0
Flight of the PEGASUS? Comparing Transformers on Few-shot and Zero-shot Multi-document Abstractive SummarizationCode0
Multi-document Summarization via Deep Learning Techniques: A Survey0
Topic-Centric Unsupervised Multi-Document Summarization of Scientific and News Articles0
WSL-DS: Weakly Supervised Learning with Distant Supervision for Query Focused Multi-Document Abstractive SummarizationCode0
Abstractive Multi-Document Summarization via Joint Learning with Single-Document SummarizationCode0
Show:102550
← PrevPage 14 of 36Next →

No leaderboard results yet.