Narratives are at the heart of information sharing. Ever since people began to share their experiences, they have connected them to form narratives. 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 unitor 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. Storylines, though, are more complex, and must take into account temporal, causal and subjective dimensions (e.g., characters’ perspectives, the good versus the bad). How storylines should be represented, how they can be extracted automatically, and how they can be evaluated are open research questions in the NLP and AI communities. In this workshop, we aim to bring together researchers working on representing and extracting narrative structures in news. In particular, we will seek to assess the state-of-the-art in event extraction and linking, as well as detecting and ranking narratives according to salience.
The reasons for this workshop are threefold:
Recent advances in NLP technology have made it feasible to look beyond scenario-driven, atomic extraction of events from single documents and work towards extracting story structures from multiple documents, while these documents are published over time as news streams.
Policy makers, NGOs, information specialists (such as journalists and librarians) and others are increasingly in need of tools that support them in finding salient stories in large amounts of information to more effectively implement policies, monitor actions of “big players” in the society and check facts. Their tasks often revolve around reconstructing cases either with respect to specific entities (e.g. person or organizations) or events (e.g. hurricane Katrina).
Storylines represent explanatory schemas that enable us to make better selections of relevant information but also projections to the future; they form a valuable potential for exploiting news data.