Multi-view Content-aware Indexing for Long Document Retrieval
Abstract: Long document question answering (DocQA) aims to answer questions from long documents over 10k words. They usually contain content structures such as sections, sub-sections, and paragraph demarcations. However, the indexing methods of long documents remain under-explored, while existing systems generally employ fixed-length chunking. As they do not consider content structures, the resultant chunks can exclude vital information or include irrelevant content. Motivated by this, we propose the **M**ulti-view **C**ontent-aware **indexing** (**MC-indexing**) for more effective long DocQA via (i) segment structured document into content chunks, and (ii) represent each content chunk in raw-text, keywords, and summary views. We highlight that MC-indexing requires neither training nor fine-tuning. Having plug-and-play capability, it can be seamlessly integrated with any retrievers to boost their performance. Besides, we propose a long DocQA dataset that includes not only question-answer pair, but also document structure and answer scope. When compared to state-of-art chunking schemes, MC-indexing has significantly increased the recall by **42.8%**, **30.0%**, **23.9%**, and **16.3%** via top k = 1.5, 3, 5, and 10 respectively. These improved scores are the average of 8 widely used retrievers (2 sparse and 6 dense) via extensive experiments.
Paper Type: Long
Research Area: Information Retrieval and Text Mining
Research Area Keywords: Content-aware chunking, multi-view indexing, retrieval, long document
Contribution Types: NLP engineering experiment
Languages Studied: English
Submission Number: 2674
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