The Role of CNL and AMR in Scalable Abstractive Summarization for Multilingual Media Monitoring

TL;DR

This paper explores the potential of Controlled Natural Language (CNL) and Abstract Meaning Representation (AMR) in scalable, multilingual media monitoring, emphasizing their roles in generating summaries from semantic graphs.

Contribution

It introduces the concept of using CNL and AMR for scalable abstractive summarization in media monitoring, highlighting their potential beyond traditional machine learning methods.

Findings

CNL can be scaled up for information extraction from normative and medical texts.

AMR graphs facilitate generating story highlights in media summaries.

The approach offers a promising alternative to purely machine learning-based summarization.

Abstract

In the era of Big Data and Deep Learning, there is a common view that machine learning approaches are the only way to cope with the robust and scalable information extraction and summarization. It has been recently proposed that the CNL approach could be scaled up, building on the concept of embedded CNL and, thus, allowing for CNL-based information extraction from e.g. normative or medical texts that are rather controlled by nature but still infringe the boundaries of CNL. Although it is arguable if CNL can be exploited to approach the robust wide-coverage semantic parsing for use cases like media monitoring, its potential becomes much more obvious in the opposite direction: generation of story highlights from the summarized AMR graphs, which is in the focus of this position paper.