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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 1–50 of 359 papers

TitleStatusHype
Attribute First, then Generate: Locally-attributable Grounded Text GenerationCode1
Embrace Divergence for Richer Insights: A Multi-document Summarization Benchmark and a Case Study on Summarizing Diverse Information from News ArticlesCode1
ODSum: New Benchmarks for Open Domain Multi-Document SummarizationCode1
Summarizing Multiple Documents with Conversational Structure for Meta-Review GenerationCode1
Multi-LexSum: Real-World Summaries of Civil Rights Lawsuits at Multiple GranularitiesCode1
Improving Multi-Document Summarization through Referenced Flexible Extraction with Credit-AwarenessCode1
PeerSum: A Peer Review Dataset for Abstractive Multi-document SummarizationCode1
Proposition-Level Clustering for Multi-Document SummarizationCode1
Proposition-Level Clustering for Multi-Document SummarizationCode1
LongT5: Efficient Text-To-Text Transformer for Long SequencesCode1
PRIMERA: Pyramid-based Masked Sentence Pre-training for Multi-document SummarizationCode1
MSˆ2: Multi-Document Summarization of Medical StudiesCode1
PRIMERA: Pyramid-based Masked Sentence Pre-training for Multi-document SummarizationCode1
HETFORMER: Heterogeneous Transformer with Sparse Attention for Long-Text Extractive SummarizationCode1
HowSumm: A Multi-Document Summarization Dataset Derived from WikiHow ArticlesCode1
SummerTime: Text Summarization Toolkit for Non-expertsCode1
TWAG: A Topic-Guided Wikipedia Abstract GeneratorCode1
Efficiently Summarizing Text and Graph Encodings of Multi-Document ClustersCode1
Transfer Learning for Sequence Generation: from Single-source to Multi-sourceCode1
MS2: Multi-Document Summarization of Medical StudiesCode1
Data Augmentation for Abstractive Query-Focused Multi-Document SummarizationCode1
Multi-XScience: A Large-scale Dataset for Extreme Multi-document Summarization of Scientific ArticlesCode1
AQuaMuSe: Automatically Generating Datasets for Query-Based Multi-Document SummarizationCode1
Quantitative Argument Summarization and Beyond: Cross-Domain Key Point AnalysisCode1
Multi-document Summarization with Maximal Marginal Relevance-guided Reinforcement LearningCode1
Summary-Source Proposition-level Alignment: Task, Datasets and Supervised BaselineCode1
Generating (Factual?) Narrative Summaries of RCTs: Experiments with Neural Multi-Document SummarizationCode1
SummPip: Unsupervised Multi-Document Summarization with Sentence Graph CompressionCode1
Pre-training via ParaphrasingCode1
DynE: Dynamic Ensemble Decoding for Multi-Document SummarizationCode1
A Large-Scale Multi-Document Summarization Dataset from the Wikipedia Current Events PortalCode1
SUPERT: Towards New Frontiers in Unsupervised Evaluation Metrics for Multi-Document SummarizationCode1
CAiRE-COVID: A Question Answering and Query-focused Multi-Document Summarization System for COVID-19 Scholarly Information ManagementCode1
GameWikiSum: a Novel Large Multi-Document Summarization DatasetCode1
Bottom-Up Abstractive SummarizationCode1
GenerationPrograms: Fine-grained Attribution with Executable ProgramsCode0
Improving Fairness of Large Language Models in Multi-document SummarizationCode0
Ask, Retrieve, Summarize: A Modular Pipeline for Scientific Literature SummarizationCode0
A Unified Retrieval Framework with Document Ranking and EDU Filtering for Multi-document Summarization—0
Estimating Optimal Context Length for Hybrid Retrieval-augmented Multi-document SummarizationCode0
Can one size fit all?: Measuring Failure in Multi-Document Summarization Domain Transfer—0
Multi2: Multi-Agent Test-Time Scalable Framework for Multi-Document Processing—0
LAG: LLM agents for Leaderboard Auto Generation on Demanding—0
LM Agents for Coordinating Multi-User Information Gathering—0
Scaling Multi-Document Event Summarization: Evaluating Compression vs. Full-Text ApproachesCode0
EventSum: A Large-Scale Event-Centric Summarization Dataset for Chinese Multi-News Documents—0
Coverage-based Fairness in Multi-document SummarizationCode0
Fair Summarization: Bridging Quality and Diversity in Extractive SummariesCode0
From Single to Multi: How LLMs Hallucinate in Multi-Document SummarizationCode0
GlobeSumm: A Challenging Benchmark Towards Unifying Multi-lingual, Cross-lingual and Multi-document News SummarizationCode0
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