This paper describes a method for language independent extractive summarization that relies on iterative graph-based ranking algorithms. Through evaluations performed on a single-document summarization task for English and Portuguese, we show that the method performs equally well regardless of the language. Moreover, we show how a metasummarizer relying on a layered application of techniques fo...

Graph is an important data structure to model complex structural data, such as chemical compounds, proteins, and XML documents. Among many graph data-based applications, sub-graph search is a key problem, which is defined as given a query Q, retrieving all graphs containing Q as a sub-graph in the graph database. Most existing sub-graph search methods try to filter out false positives (graphs t...

Keyword search has been popularly used to query graph data. Due to the lack of structure support, a keyword query might generate an excessive number of matches, referred to as “answer graphs”, that could include different relationships among keywords. An ignored yet important task is to group and summarize answer graphs that share similar structures and contents for better query interpretation ...

Automatic text summarization methods generate a shorter version of the input to assist reader in gaining quick yet informative gist. Existing generally focus on single aspect when selecting sentences, causing potential loss essential information. In this study, we propose domain-specific method that models document as multi-layer graph enable multiple features be processed at same time. The use...

Question Answering (QA) systems over Knowledge Graphs (KGs) (KGQA) automatically answer natural language questions using triples contained in a KG. The key idea is to represent and entities of KG as low-dimensional embeddings. Previous KGQAs have attempted Graph Embedding (KGE) Deep Learning (DL) methods. However, KGEs are too shallow capture the expressive features DL methods process each trip...

Since most of news articles report several events and these events are referred in many related documents, we propose an event-based approach to visualize documents as graph on different conceptual granularities. With graphbased ranking algorithm, we illustrate the application of document graph to multi-document summarization. Experiments on DUC data indicate that our approach is competitive wi...

Graphs naturally represent information ranging from links between webpages, to friendships in social networks, to connections between neurons in our brains. These graphs often span billions of nodes and interactions between them. Within this deluge of interconnected data, how can we find the most important structures and summarize them? How can we efficiently visualize them? How can we detect a...

We present summarization and spoken term detection (STD) approaches that take into account similarities between utterances to be scored for summary extraction or ranking in STD. A graph is constructed in which each utterance is a node. Similar utterances are connected by edges, with the edge weights representing the degree of similarity. The similarity for summarization is topical similarity; t...

While advances in computing resources have made processing enormous amounts of data possible, human ability to identify patterns in such data has not scaled accordingly. Efficient computational methods for condensing and simplifying data are thus becoming vital for extracting actionable insights. In particular, while data summarization techniques have been studied extensively, only recently has...