  The paper steps outside the comfort-zone of the traditional NLP tasks like
automatic speech recognition (ASR) and machine translation (MT) to addresses
two novel problems arising in the automated multilingual news monitoring:
segmentation of the TV and radio program ASR transcripts into individual
stories, and clustering of the individual stories coming from various sources
and languages into storylines. Storyline clustering of stories covering the
same events is an essential task for inquisitorial media monitoring. We address
these two problems jointly by engaging the low-dimensional semantic
representation capabilities of the sequence to sequence neural translation
models. To enable joint multi-task learning for multilingual neural translation
of morphologically rich languages we replace the attention mechanism with the
sliding-window mechanism and operate the sequence to sequence neural
translation model on the character-level rather than on the word-level. The
story segmentation and storyline clustering problem is tackled by examining the
low-dimensional vectors produced as a side-product of the neural translation
process. The results of this paper describe a novel approach to the automatic
story segmentation and storyline clustering problem.
