Exploring Communicative AI: Reflections from a Swedish Newsroom

This article contributes to the emerging field of research on computational journalism with a practical illustration of an attempt to utilize Machine Learning to generate Search Engine Optimized headlines in a major Swedish newsroom. By using its technical results as a springboard for reflections among internal stakeholders, the experiment serves as a catalyzing innovation revealing deliberations on computational approaches in journalism in general and communicative Artificial Intelligence (AI) in specific. The study concludes with three ideas to support decision makers involved in evaluating potential use cases for communicative AI in journalism

Order-Preserving Abstractive Summarization for Spoken Content Based on Connectionist Temporal Classification

Connectionist temporal classification (CTC) is a powerful approach for sequence-to-sequence learning, and has been popularly used in speech recognition. The central ideas of CTC include adding a label "blank" during training. With this mechanism, CTC eliminates the need of segment alignment, and hence has been applied to various sequence-to-sequence learning problems. In this work, we applied CTC to abstractive summarization for spoken content. The "blank" in this case implies the corresponding input data are less important or noisy; thus it can be ignored. This approach was shown to outperform the existing methods in term of ROUGE scores over Chinese Gigaword and MATBN corpora. This approach also has the nice property that the ordering of words or characters in the input documents can be better preserved in the generated summaries.Comment: Accepted by Interspeech 201