mlsanityData to 7 Oct 2026

Summarization with Graphical Elements

arXiv 2204.07551 ↗First posted 15 Apr 2022cs.CL cs.AIcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 34 of its 59 references that are in the map. First posted on arXiv 15 Apr 2022.

Builds on

34 of 59 references.

References · 34

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22 more
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Citing papers per year: 2022: 2.

2 papers.

  1. 2022PREME: Preference-based Meeting Exploration through an Interactive Questionnairecited by 2
  2. 2022High-Resource Methodological Bias in Low-Resource Investigationscited by 1