With the increasing use of online communication platforms, such as email, twitter, and messaging applications, we are faced with a growing amount of data that combine content (what is said), time (when), and user (by whom) information. An important computational challenge is to analyze these data, discover meaningful patterns, and understand what is happening. We consider the problem of mining online communication data and finding top-k temporal events. We define a temporal event to be a coherent topic that is discussed frequently, in a relatively short time span, while the information ow of the event respects the underlying network structure. We construct our model for detecting temporal events in two steps. We first introduce the notion of interaction meta-graph, which connects associated interactions. Using this notion, we define a temporal event to be a subset of interactions that (i) are topically and temporally close and (ii) correspond to a tree that captures the information ow. Finding the best temporal event leads to budget version of the prize-collecting Steiner-tree (PCST) problem, which we solve using three different methods: a greedy approach, a dynamic-programming algorithm, and an adaptation to an existing approximation algorithm. The problem of finding the top- k events among a set of candidate events maps to maximum set-cover problem, and thus, solved by greedy. We compare and analyze our algorithms in both synthetic and real datasets, such as twitter and email communication. The results show that our methods are able to detect meaningful temporal events.
Extractive methods for multi-document summarization are mainly governed by information overlap, coherence, and content constraints. We present an unsupervised probabilistic approach to model the hidden abstract concepts across documents as well as the correlation between these concepts, to generate topically coherent and non-redundant summaries. Based on human evaluations our models generate summaries with higher linguistic quality in terms of coherence, readability, and redundancy compared to benchmark systems. Although our system is unsupervised and optimized for topical coherence, we achieve a 44.1 ROUGE on the DUC-07 test set, roughly in the range of state-of-the-art supervised models. Due to the sheer volume of text generated by a micro log site like Twitter, it is often difficult to fully understand what is being said about various topics. In an attempt to understand micro logs better, this paper compares algorithms for extractive summarization of micro log posts. We present two algorithms that produce summaries by selecting several posts from a given set. We evaluate the generated summaries by comparing them to both manually produced summaries and summaries produced by several leading traditional summarization systems. In order to shed light on the special nature of Twitter posts, we include extensive analysis of our results, some of which are unexpected.