PRISM: Fine-Grained Paper-to-Paper Retrieval with Multi-Aspect-Aware Query Optimization

• URL: http://arxiv.org/abs/2507.10057v1
• Date: Mon, 14 Jul 2025 08:41:53 GMT
• Title: PRISM: Fine-Grained Paper-to-Paper Retrieval with Multi-Aspect-Aware Query Optimization
• Authors: Sangwoo Park, Jinheon Baek, Soyeong Jeong, Sung Ju Hwang,
• Abstract summary: PRISM is a document-to-document retrieval method that introduces multiple, fine-grained representations for both the query and candidate papers.<n>We present SciFullBench, a novel benchmark in which the complete and segmented context of full papers for both queries and candidates is available.<n>Experiments show that PRISM improves performance by an average of 4.3% over existing retrieval baselines.
• Score: 61.783280234747394
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Scientific paper retrieval, particularly framed as document-to-document retrieval, aims to identify relevant papers in response to a long-form query paper, rather than a short query string. Previous approaches to this task have focused on abstracts, embedding them into dense vectors as surrogates for full documents and calculating similarity across them, although abstracts provide only sparse and high-level summaries. To address this, we propose PRISM, a novel document-to-document retrieval method that introduces multiple, fine-grained representations for both the query and candidate papers. In particular, each query paper is decomposed into multiple aspect-specific views and individually embedded, which are then matched against candidate papers similarity segmented to consider their multifaceted dimensions. Moreover, we present SciFullBench, a novel benchmark in which the complete and segmented context of full papers for both queries and candidates is available. Then, experimental results show that PRISM improves performance by an average of 4.3% over existing retrieval baselines.

Related papers

• LLM-Based Compact Reranking with Document Features for Scientific Retrieval [30.341167520613197]
  We propose a training-free, model-agnostic semantic reranking framework for scientific retrieval called CoRank.<n>CoRank involves three stages: offline extraction of document-level features, coarse reranking using these compact representations, and fine-grained reranking on full texts of the top candidates from stage.<n> Experiments on LitSearch and CSFCube show that CoRank significantly improves reranking performance across different LLM backbones.
  arXiv  Detail & Related papers  (2025-05-19T22:10:27Z)
• 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 MMDocIR, that encompasses two distinct tasks: page-level and layout-level retrieval.<n>The MMDocIR benchmark comprises a rich dataset featuring 1,685 questions annotated by experts and 173,843 questions with bootstrapped labels.
  arXiv  Detail & Related papers  (2025-01-15T14:30:13Z)
• 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)
• DAPR: A Benchmark on Document-Aware Passage Retrieval [57.45793782107218]
  We propose and name this task emphDocument-Aware Passage Retrieval (DAPR) While analyzing the errors of the State-of-The-Art (SoTA) passage retrievers, we find the major errors (53.5%) are due to missing document context. Our created benchmark enables future research on developing and comparing retrieval systems for the new task.
  arXiv  Detail & Related papers  (2023-05-23T10:39:57Z)
• Mining both Commonality and Specificity from Multiple Documents for Multi-Document Summarization [1.4629756274247374]
  The multi-document summarization task requires the designed summarizer to generate a short text that covers the important information of original documents. This paper proposes a multi-document summarization approach based on hierarchical clustering of documents.
  arXiv  Detail & Related papers  (2023-03-05T14:25:05Z)
• Generating a Structured Summary of Numerous Academic Papers: Dataset and Method [20.90939310713561]
  We propose BigSurvey, the first large-scale dataset for generating comprehensive summaries of numerous academic papers on each topic. We collect target summaries from more than seven thousand survey papers and utilize their 430 thousand reference papers' abstracts as input documents. To organize the diverse content from dozens of input documents, we propose a summarization method named category-based alignment and sparse transformer (CAST)
  arXiv  Detail & Related papers  (2023-02-09T11:42:07Z)
• 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)
• Multi-View Document Representation Learning for Open-Domain Dense Retrieval [87.11836738011007]
  This paper proposes a multi-view document representation learning framework. It aims to produce multi-view embeddings to represent documents and enforce them to align with different queries. Experiments show our method outperforms recent works and achieves state-of-the-art results.
  arXiv  Detail & Related papers  (2022-03-16T03:36:38Z)
• 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)
• Text Summarization with Latent Queries [60.468323530248945]
  We introduce LaQSum, the first unified text summarization system that learns Latent Queries from documents for abstractive summarization with any existing query forms. Under a deep generative framework, our system jointly optimize a latent query model and a conditional language model, allowing users to plug-and-play queries of any type at test time. Our system robustly outperforms strong comparison systems across summarization benchmarks with different query types, document settings, and target domains.
  arXiv  Detail & Related papers  (2021-05-31T21:14:58Z)

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.