The paper proposes a new approach to single-document extractive summarization by constructing a sentence hypergraph where sentences are nodes and keywords or named entities are hyperedges. A greedy algorithm is then used to find a dominating set of this hypergraph, which yields the sentences that compose the summary. The study compares this hypergraph-based method with existing graph-based summarization techniques.
arXiv:2607. 20506v1 Announce Type: new Abstract: GraphRAG enables deeper reasoning by structuring knowledge as graphs but struggles with n-ary facts.
arXiv:2607. 10806v1 Announce Type: cross Abstract: Quantifying abstractiveness in generated summaries is essential for evaluating summarization models beyond surface-level metrics like ROUGE.
arXiv:2608. 19200v1 Announce Type: cross Abstract: Text summarization refers to the task of condensing a document into a shorter version while preserving its key information.
arXiv:2606. 03867v1 Announce Type: cross Abstract: Multi-Document Summarization (MDS) plays a critical role in distilling essential information from collections of textual data.
arXiv:2606. 18075v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) has emerged as a paradigm for enhancing large language models (LLMs) with external knowledge, yet existing graph-based methods face a fundamental limitation: entity-centric and chunk-centric approaches operate on representations anchored to original text without true knowledge fusion.
arXiv:2607. 10969v1 Announce Type: cross Abstract: Given a large graph, how to generate a compact summary graph that is configurable by the user and supports multiple graph queries with either no loss or with high accuracy?
arXiv:2606. 28367v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) is routinely extended with methods meant to improve retrieval: query expansion, hierarchical and cross-document summarization, graph-based expansion, per-query routing, rank fusion, and corrective re-retrieval.
arXiv:2606. 19591v1 Announce Type: cross Abstract: In this technical report, we focus on solving the challenge of Vietnamese multi-document abstractive summarization, introduced in the International Workshop on Vietnamese Language and Speech Processing (VLSP) 2022.
Given a large graph, how to generate a compact summary graph that is configurable by the user and supports multiple graph queries with either no loss or with high accuracy? The ever growing size of graph datasets makes the above question on graph summarization very pertinent.
arXiv:2606. 08445v1 Announce Type: cross Abstract: Meeting documents are challenging to summarize due to their length and complex conversational structure.
arXiv:2407. 10486v3 Announce Type: replace Abstract: Query-focused summarization (QFS) aims to produce summaries that answer particular questions of interest, enabling greater user control and personalization.
arXiv:2606. 05494v1 Announce Type: cross Abstract: Automatic text summarization has become increasingly important due to the rapid growth of digital textual information.