research-article

The Power of Selecting Key Blocks with Local Pre-ranking for Long Document Information Retrieval

Authors:

Diana Nicoleta Popa,

Yagmur Gizem Cinar,

Eric GaussierAuthors Info & Claims

ACM Transactions on Information Systems, Volume 41, Issue 3

Article No.: 73, Pages 1 - 35

https://doi.org/10.1145/3568394

Published: 07 February 2023 Publication History

Abstract

On a wide range of natural language processing and information retrieval tasks, transformer-based models, particularly pre-trained language models like BERT, have demonstrated tremendous effectiveness. Due to the quadratic complexity of the self-attention mechanism, however, such models have difficulties processing long documents. Recent works dealing with this issue include truncating long documents, in which case one loses potential relevant information, segmenting them into several passages, which may lead to miss some information and high computational complexity when the number of passages is large, or modifying the self-attention mechanism to make it sparser as in sparse-attention models, at the risk again of missing some information. We follow here a slightly different approach in which one first selects key blocks of a long document by local query-block pre-ranking, and then few blocks are aggregated to form a short document that can be processed by a model such as BERT. Experiments conducted on standard Information Retrieval datasets demonstrate the effectiveness of the proposed approach.

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Index Terms

The Power of Selecting Key Blocks with Local Pre-ranking for Long Document Information Retrieval
1. Information systems
  1. Information retrieval
    1. Document representation
    2. Retrieval models and ranking

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cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 41, Issue 3

July 2023

890 pages

ISSN:1046-8188

EISSN:1558-2868

DOI:10.1145/3582880

Editor:
Min Zhang
Tsinghua University, China

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Publication History

Published: 07 February 2023

Online AM: 17 October 2022

Accepted: 29 September 2022

Revised: 30 July 2022

Received: 03 December 2021

Published in TOIS Volume 41, Issue 3

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