Current tasks and methods in Document Understanding aims to process documents
as single elements. However, documents are usually organized in collections
(historical records, purchase invoices), that provide context useful for their
interpretation. To address this problem, we introduce Document Collection
Visual Question Answering (DocCVQA) a new dataset and related task, where
questions are posed over a whole collection of document images and the goal is
not only to provide the answer to the given question, but also to retrieve the
set of documents that contain the information needed to infer the answer. Along
with the dataset we propose a new evaluation metric and baselines which provide
further insights to the new dataset and task.

文章提出一个新的任务 —— 使用文献收集进行文档理解，提出了一个新的数据集和新的评估指标和基线模型

文档集合视觉问答

Document Collection Visual Question Answering

The need to organize a large collection in a manner that facilitates human
comprehension is crucial given the ever-increasing volumes of information. In
this work, we present PDC (probabilistic distributional clustering), a novel
algorithm that, given a document collection, computes disjoint term sets
representing topics in the collection. The algorithm relies on probabilities of
word co-occurrences to partition the set of terms appearing in the collection
of documents into disjoint groups of related terms. In this work, we also
present an environment to visualize the computed topics in the term space and
retrieve the most related PubMed articles for each group of terms. We
illustrate the algorithm by applying it to PubMed documents on the topic of
suicide. Suicide is a major public health problem identified as the tenth
leading cause of death in the US. In this application, our goal is to provide a
global view of the mental health literature pertaining to the subject of
suicide, and through this, to help create a rich environment of multifaceted
data to guide health care researchers in their endeavor to better understand
the breadth, depth and scope of the problem. We demonstrate the usefulness of
the proposed algorithm by providing a web portal that allows mental health
researchers to peruse the suicide-related literature in PubMed.

本文提出一种名为 PDC（概率分布聚类）的新算法，可以将文档集合中的词项划分为代表集合中的主题，从而帮助研究者更好地理解特定主题的文献资料，同时提供一个可视化的环境，以支持 PubMed 中相关领域的多面数据查询。