Leveraging Semantic Annotations for Event-focused Search & Summarization

degree of redundancy has made it hard for a consumer to retrospect on past events. A plausible solution is to
link semantically similar information contained across the different sources to enforce a structure thereby
providing multiple access paths to relevant information. Keeping this larger goal in view, this work uses
Wikipedia and online news articles as two prominent yet disparate information sources to address the following
three problems:
•    We address a linking problem to connect Wikipedia excerpts to news articles by casting it into an IR task.
Our novel approach integrates time, geolocations, and entities with text to identify relevant documents that
can be linked to a given excerpt.
•    We address an unsupervised extractive multi-document summarization task to generate a fixed-length event
digest that facilitates efficient consumption of information contained within a large set of documents. Our novel
approach proposes an ILP for global inference across text, time, geolocations, and entities associated with the event.
•    To estimate temporal focus of short event descriptions, we present a semi-supervised approach that leverages
redundancy within a longitudinal news collection to estimate accurate probabilistic time models.
Extensive experimental evaluations demonstrate the effectiveness and viability of our proposed approaches
towards achieving the larger goal.