Event Digest: A Holistic View on Past Events

For a general user, easy access to vast amounts of online information available on past events has made retrospection much harder. We propose a problem of automatic event digest generation to aid effective and efficient retrospection. For this, in addition to text, a digest should maximize the reportage of time, geolocations, and entities to present a holistic view on the past event of interest.

We propose a novel divergence-based framework that selects excerpts from an initial set of pseudo-relevant documents, such that the overall relevance is maximized, while avoiding redundancy in text, time, geolocations, and named entities, by treating them as independent dimensions of an event. Our method formulates the problem as an Integer Linear Program (ILP) for global inference to diversify across the event dimensions. Relevance and redundancy measures are defined based on JS-divergence between independent query and excerpt models estimated for each event dimension. Elaborate experiments on three real-world datasets are conducted to compare our methods against the state-of-the-art from the literature. Using Wikipedia articles as gold standard summaries in our evaluation, we find that the most holistic digest of an event is generated with our method that integrates all event dimensions. We compare all methods using standard Rouge-1, -2, and -SU4 along with Rouge-NP, and a novel weighted variant of Rouge.