A Unified Retrieval Framework with Document Ranking and EDU Filtering for Multi-document Summarization

• URL: http://arxiv.org/abs/2504.16711v1
• Date: Wed, 23 Apr 2025 13:41:10 GMT
• Title: A Unified Retrieval Framework with Document Ranking and EDU Filtering for Multi-document Summarization
• Authors: Shiyin Tan, Jaeeon Park, Dongyuan Li, Renhe Jiang, Manabu Okumura,
• Abstract summary: Current methods apply truncation after the retrieval process to fit the context length.<n>We propose a novel retrieval-based framework that integrates query selection and document ranking.<n>We evaluate our framework on multiple MDS datasets, demonstrating consistent improvements in ROUGE metrics.
• Score: 18.13855430873805
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the field of multi-document summarization (MDS), transformer-based models have demonstrated remarkable success, yet they suffer an input length limitation. Current methods apply truncation after the retrieval process to fit the context length; however, they heavily depend on manually well-crafted queries, which are impractical to create for each document set for MDS. Additionally, these methods retrieve information at a coarse granularity, leading to the inclusion of irrelevant content. To address these issues, we propose a novel retrieval-based framework that integrates query selection and document ranking and shortening into a unified process. Our approach identifies the most salient elementary discourse units (EDUs) from input documents and utilizes them as latent queries. These queries guide the document ranking by calculating relevance scores. Instead of traditional truncation, our approach filters out irrelevant EDUs to fit the context length, ensuring that only critical information is preserved for summarization. We evaluate our framework on multiple MDS datasets, demonstrating consistent improvements in ROUGE metrics while confirming its scalability and flexibility across diverse model architectures. Additionally, we validate its effectiveness through an in-depth analysis, emphasizing its ability to dynamically select appropriate queries and accurately rank documents based on their relevance scores. These results demonstrate that our framework effectively addresses context-length constraints, establishing it as a robust and reliable solution for MDS.

Related papers

• Learning More Effective Representations for Dense Retrieval through Deliberate Thinking Before Search [65.53881294642451]
  Deliberate Thinking based Dense Retriever (DEBATER)<n>DEBATER enhances recent dense retrievers by enabling them to learn more effective document representations through a step-by-step thinking process.<n> Experimental results show that DEBATER significantly outperforms existing methods across several retrieval benchmarks.
  arXiv  Detail & Related papers  (2025-02-18T15:56:34Z)
• Enhanced Retrieval of Long Documents: Leveraging Fine-Grained Block Representations with Large Language Models [24.02950598944251]
  We introduce a novel, fine-grained approach aimed at enhancing the accuracy of relevance scoring for long documents.<n>Our methodology firstly segments a long document into blocks, each of which is embedded using an LLM.<n>We aggregate the query-block relevance scores through a weighted sum method, yielding a comprehensive score for the query with the entire document.
  arXiv  Detail & Related papers  (2025-01-28T16:03:52Z)
• MMDocIR: Benchmarking Multi-Modal Retrieval for Long Documents [26.39534684408116]
  This work introduces a new benchmark, named as MMDocIR, encompassing two distinct tasks: page-level and layout-level retrieval.<n>The MMDocIR benchmark comprises a rich dataset featuring expertly annotated labels for 1,685 questions and bootstrapped labels for 173,843 questions.
  arXiv  Detail & Related papers  (2025-01-15T14:30:13Z)
• VisDoM: Multi-Document QA with Visually Rich Elements Using Multimodal Retrieval-Augmented Generation [100.06122876025063]
  This paper introduces VisDoMBench, the first comprehensive benchmark designed to evaluate QA systems in multi-document settings. We propose VisDoMRAG, a novel multimodal Retrieval Augmented Generation (RAG) approach that simultaneously utilizes visual and textual RAG.
  arXiv  Detail & Related papers  (2024-12-14T06:24:55Z)
• Unified Multimodal Interleaved Document Representation for Retrieval [57.65409208879344]
  We propose a method that holistically embeds documents interleaved with multiple modalities.<n>We merge the representations of segmented passages into one single document representation.<n>We show that our approach substantially outperforms relevant baselines.
  arXiv  Detail & Related papers  (2024-10-03T17:49:09Z)
• ODSum: New Benchmarks for Open Domain Multi-Document Summarization [30.875191848268347]
  Open-domain Multi-Document Summarization (ODMDS) is a critical tool for condensing vast arrays of documents into coherent, concise summaries. We propose a rule-based method to process query-based document summarization datasets into ODMDS datasets.
  arXiv  Detail & Related papers  (2023-09-16T11:27:34Z)
• Query2doc: Query Expansion with Large Language Models [69.9707552694766]
  The proposed method first generates pseudo- documents by few-shot prompting large language models (LLMs) query2doc boosts the performance of BM25 by 3% to 15% on ad-hoc IR datasets. Our method also benefits state-of-the-art dense retrievers in terms of both in-domain and out-of-domain results.
  arXiv  Detail & Related papers  (2023-03-14T07:27:30Z)
• GERE: Generative Evidence Retrieval for Fact Verification [57.78768817972026]
  We propose GERE, the first system that retrieves evidences in a generative fashion. The experimental results on the FEVER dataset show that GERE achieves significant improvements over the state-of-the-art baselines.
  arXiv  Detail & Related papers  (2022-04-12T03:49:35Z)
• CODER: An efficient framework for improving retrieval through COntextualized Document Embedding Reranking [11.635294568328625]
  We present a framework for improving the performance of a wide class of retrieval models at minimal computational cost. It utilizes precomputed document representations extracted by a base dense retrieval method. It incurs a negligible computational overhead on top of any first-stage method at run time, allowing it to be easily combined with any state-of-the-art dense retrieval method.
  arXiv  Detail & Related papers  (2021-12-16T10:25:26Z)
• Value Retrieval with Arbitrary Queries for Form-like Documents [50.5532781148902]
  We propose value retrieval with arbitrary queries for form-like documents. Our method predicts target value for an arbitrary query based on the understanding of layout and semantics of a form. We propose a simple document language modeling (simpleDLM) strategy to improve document understanding on large-scale model pre-training.
  arXiv  Detail & Related papers  (2021-12-15T01:12:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.