Improving Event Detection with Abstract Meaning Representation News Clustering Approach Based on Discourse Text Structure Expanding the Horizons: Adding a New Language to the News Personalization System Cross-document Non-fiction Narrative Alignment News Clustering Approach Based on Discourse Text S

Order copies of this and other ACL proceedings from: Narratives are at the heart of information sharing. Ever since people began to share their experiences, they have connected them to form narratives. The study of storytelling and the field of literary theory called narratology have developed complex frameworks and models related to various aspects of narrative such as plots structures, narrative embeddings, characters' perspectives, reader response, point of view, narrative voice, narrative goals, and many others. These notions from narratology have been applied mainly in Artificial Intelligence and to model formal semantic approaches to narratives (e.g. Plot Units developed by Lehnert (1981)). In recent years, computational narratology has qualified as an autonomous field of study and research. Narrative has been the focus of a number of workshops and conferences (AAAI Symposia, Interactive Storytelling Conference (ICIDS), Computational Models of Narrative). Furthermore, reference annotation schemes for narratives have been proposed (NarrativeML by Mani (2013)). The majority of the previous work on narratives and narrative structures have mainly focused on the analysis of fictitious texts. However, modern day news reports still reflect this narrative structure, but they have proven difficult for automatic tools to summarise, structure, or connect to other reports. This difficulty is partly rooted in the fact that most text processing tools focus on extracting relatively simple structures from the local lexical environment, and concentrate on the document as a unit or on even smaller units such as sentences or phrases, rather than cross-document connections. However, current information needs demand a move towards multidimensional and distributed representations which take into account the connections between all relevant elements involved in a " story ". Additionally, most work on cross-document temporal processing focuses on linear timelines, i.e. representations of chronologically ordered events in time (for instance, the Event Narrative Event Chains by Chambers (2011), or the SemEval 2015 Task 4: Cross Document TimeLines by Minard et al. (2014)). Storylines, though, are more complex, and must take into account temporal, causal and subjective dimensions. How storylines should be represented and annotated, how they can be extracted automatically, and how they can be evaluated are open research questions in the NLP and AI communities. The workshop aimed to bring together researchers from different communities working on representing and extracting narrative structures in news, a text genre which is highly used in NLP but which has received little attention with respect to narrative structure, representation and …