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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 241250 of 359 papers

TitleStatusHype
LQVSumm: A Corpus of Linguistic Quality Violations in Multi-Document Summarization0
Massive Multi-Document Summarization of Product Reviews with Weak Supervision0
Measuring Semantic Similarity for Bengali Tweets Using WordNet0
Mining both Commonality and Specificity from Multiple Documents for Multi-Document Summarization0
Mining the Gaps: Towards Polynomial Summarization0
Mitigating Framing Bias with Polarity Minimization Loss0
Monolingual Distributional Similarity for Text-to-Text Generation0
Monolingual versus Multilingual BERTology for Vietnamese Extractive Multi-Document Summarization0
Monolingual vs multilingual BERTology for Vietnamese extractive multi-document summarization0
Moving beyond word lists: towards abstractive topic labels for human-like topics of scientific documents0
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