The Role of CNL and AMR in Scalable Abstractive Summarization for Multilingual Media Monitoring
This work addresses the problem of scalable multilingual media monitoring summarization, but it is incremental as it builds on existing CNL and AMR concepts without introducing new methods.
This position paper argues that while Controlled Natural Language (CNL) has limited applicability for robust semantic parsing in media monitoring, it shows greater potential for generating story highlights from summarized Abstract Meaning Representation (AMR) graphs, focusing on scalable abstractive summarization for multilingual contexts.
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.