Summarization with Graphical Elements

TL;DR

This paper introduces a new summarization task that incorporates graphical elements to better meet user needs, supported by a high-quality dataset and baseline methods to encourage further research.

Contribution

The paper proposes the novel task of summarization with graphical elements, creating a dataset and baseline methods to advance this emerging research area.

Findings

Summaries with graphical elements are helpful for a significant portion of users.

The task is challenging but feasible, as shown by baseline method performance.

The dataset supports future research in this direction.

Abstract

Automatic text summarization has experienced substantial progress in recent years. With this progress, the question has arisen whether the types of summaries that are typically generated by automatic summarization models align with users' needs. Ter Hoeve et al (2020) answer this question negatively. Amongst others, they recommend focusing on generating summaries with more graphical elements. This is in line with what we know from the psycholinguistics literature about how humans process text. Motivated from these two angles, we propose a new task: summarization with graphical elements, and we verify that these summaries are helpful for a critical mass of people. We collect a high quality human labeled dataset to support research into the task. We present a number of baseline methods that show that the task is interesting and challenging. Hence, with this work we hope to inspire a new line of research within the automatic summarization community.