  Most of the existing information extraction frameworks (Wadden et al., 2019;
Veysehet al., 2020) focus on sentence-level tasks and are hardly able to
capture the consolidated information from a given document. In our endeavour to
generate precise document-level information frames from lengthy textual
records, we introduce the task of Information Aggregation or Argument
Aggregation. More specifically, our aim is to filter irrelevant and redundant
argument mentions that were extracted at a sentence level and render a document
level information frame. Majority of the existing works have been observed to
resolve related tasks of document-level event argument extraction (Yang et al.,
2018a; Zheng et al., 2019a) and salient entity identification (Jain et
al.,2020) using supervised techniques. To remove dependency from large amounts
of labelled data, we explore the task of information aggregation using
weakly-supervised techniques. In particular, we present an extractive algorithm
with multiple sieves which adopts active learning strategies to work
efficiently in low-resource settings. For this task, we have annotated our own
test dataset comprising of 131 document information frames and have released
the code and dataset to further research prospects in this new domain. To the
best of our knowledge, we are the first to establish baseline results for this
task in English. Our data and code are publicly available at
https://github.com/DebanjanaKar/ArgFuse.

Computation and Language