Towards Annotating and Creating Sub-Sentence Summary Highlights

TL;DR

This paper introduces a method for generating concise sub-sentence highlights by annotating important sub-sentences and training classifiers, providing new benchmarks and reducing complexity in summary highlight creation.

Contribution

It proposes a novel approach to sub-sentence highlight annotation and classification, along with new benchmarks for this task.

Findings

Developed a joint selection and identification framework.

Provided new datasets and baseline results.

Reduced complexity in sub-sentence highlight generation.

Abstract

Highlighting is a powerful tool to pick out important content and emphasize. Creating summary highlights at the sub-sentence level is particularly desirable, because sub-sentences are more concise than whole sentences. They are also better suited than individual words and phrases that can potentially lead to disfluent, fragmented summaries. In this paper we seek to generate summary highlights by annotating summary-worthy sub-sentences and teaching classifiers to do the same. We frame the task as jointly selecting important sentences and identifying a single most informative textual unit from each sentence. This formulation dramatically reduces the task complexity involved in sentence compression. Our study provides new benchmarks and baselines for generating highlights at the sub-sentence level.