How do graph clustering techniques compare with respect to their summarization power? How well can they summarize a million-node graph with a few representative structures? Graph clustering or community detection algorithms can summarize a graph in terms of coherent and tightly connected clusters. In this paper, we compare and contrast different techniques: METIS, Louvain, spectral clustering, ...

Since Thai language lacks word/phrase/sentence boundaries, document summarization in Thai needs investigations in unit segmentation, unit selection, redundancy removal and evaluation dataset construction. In this work, we have proposed Thai Elementary Discourse Unit (TEDU) and a three-stage method of Thai multidocument summarization, i.e., unit segmentation, unit-graph formulation, and unit sel...

Document summarization is a technique aimed to automatically extract main ideas from electronic documents. In this paper, we propose a novel algorithm, called TriangleSum for key sentence extraction from single document based on graph theory. The algorithm builds a dependency graph for the underlying document based on co-occurrence relation as well as syntactic dependency relations. The nodes r...

This paper introduces the Tuple Graph (TuG) synopses, a new class of data summaries that enable accurate selectivity estimates for complex relational queries. The proposed summarization framework adopts a “semi-structured” view of the relational database, modeling a relational data set as a graph of tuples and join queries as graph traversals respectively. The key idea is to approximate the str...

Document summarization is a technique aimed to automatically extract the main ideas from electronic documents. With the fast increase of electronic documents available on the network, techniques for making efficient use of such documents become increasingly important. In this paper, we propose a novel algorithm, called TriangleSum for single document summarization based on graph theory. The alg...

We present a novel abstractive summarization framework that draws on the recent development of a treebank for the Abstract Meaning Representation (AMR). In this framework, the source text is parsed to a set of AMR graphs, the graphs are transformed into a summary graph, and then text is generated from the summary graph. We focus on the graph-tograph transformation that reduces the source semant...

today, with rapid growth of the world wide web and creation of internet sites and online text resources, text summarization issue is highly attended by various researchers. extractive-based text summarization is an important summarization method which is included of selecting the top representative sentences from the input document. when, we are facing into large data volume documents, the extr...