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 Multi-view Content-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.

通过多视角内容感知索引（MC 索引）来提高长文档问答（DocQA）的性能，无需训练或微调。与现有的固定长度切块方式相比，MC 索引显著提高了召回率，并能与任何检索器无缝集成。

多视图内容感知长文档检索

Multi-view Content-aware Indexing for Long Document Retrieval

Long document question answering is a challenging task due to its demands for
complex reasoning over long text. Previous works usually take long documents as
non-structured flat texts or only consider the local structure in long
documents. However, these methods usually ignore the global structure of the
long document, which is essential for long-range understanding. To tackle this
problem, we propose Compressive Graph Selector Network (CGSN) to capture the
global structure in a compressive and iterative manner. The proposed model
mainly focuses on the evidence selection phase of long document question
answering. Specifically, it consists of three modules: local graph network,
global graph network and evidence memory network. Firstly, the local graph
network builds the graph structure of the chunked segment in token, sentence,
paragraph and segment levels to capture the short-term dependency of the text.
Secondly, the global graph network selectively receives the information of each
level from the local graph, compresses them into the global graph nodes and
applies graph attention to the global graph nodes to build the long-range
reasoning over the entire text in an iterative way. Thirdly, the evidence
memory network is designed to alleviate the redundancy problem in the evidence
selection by saving the selected result in the previous steps. Extensive
experiments show that the proposed model outperforms previous methods on two
datasets.

本文提出了一种基于压缩图选择网络来捕捉全局结构的长文本问答模型，主要集中在长文档问答的证据选择阶段。通过本文的大量实验研究证明，该模型在两个数据集上优于以前的方法。