Log In Sign Up

Summarization with Graphical Elements

by   Maartje ter Hoeve, et al.

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.


page 16

page 19

page 20


SQuALITY: Building a Long-Document Summarization Dataset the Hard Way

Summarization datasets are often assembled either by scraping naturally ...

What Makes a Good Summary? Reconsidering the Focus of Automatic Summarization

Automatic text summarization has enjoyed great progress over the last ye...

Automatic text summarization: What has been done and what has to be done

Summaries are important when it comes to process huge amounts of informa...

Neural Code Summarization

Code summarization is the task of generating readable summaries that are...

Generating summaries tailored to target characteristics

Recently, research efforts have gained pace to cater to varied user pref...

Intweetive Text Summarization

The amount of user generated contents from various social medias allows ...

Automatic Summarization of Online Debates

Debate summarization is one of the novel and challenging research areas ...