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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

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Showing 41–50 of 359 papers

TitleStatusHype
Adapting Neural Single-Document Summarization Model for Abstractive Multi-Document Summarization: A Pilot Study—0
Benchmarking LLMs on the Semantic Overlap Summarization Task—0
A Multi-level Annotated Corpus of Scientific Papers for Scientific Document Summarization and Cross-document Relation Discovery—0
ACM -- Attribute Conditioning for Abstractive Multi Document Summarization—0
Abstractive Multi-Document Summarization via Phrase Selection and Merging—0
A Supervised Aggregation Framework for Multi-Document Summarization—0
A Multi-Document Coverage Reward for RELAXed Multi-Document Summarization—0
A Subjective Logic Framework for Multi-Document Summarization—0
Assessing the performance of Olelo, a real-time biomedical question answering application—0
A Method of Accounting Bigrams in Topic Models—0
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