Thai Multi-Document Summarization: Unit Segmentation, Unit-Graph Formulation, and Unit Selection

There have been several challenges in summarization of Thai multiple documents since Thai language itself lacks of explicit word/phrase/sentence boundaries. This paper gives definition of Thai Elementary Discourse Unit (TEDU) and then presents our three-stage summarization process. Towards implementation of this process, we propose unit segmentation using TEDUs and their derivatives, unit-graph formation using iterative unit weighting and cosine similarity, and unit selection using highest-weight priority, redundancy removal, and post-selection weight recalculation. To examine performance of the proposed methods, a number of experiments are conducted using fifty sets of Thai news articles with their manually constructed reference summary. By three common evaluation measures of ROUGE-1, ROUGE-2, and ROUGE-SU4, the results evidence that (1) our TEDU-based summarization outperforms paragraph-based summarization, (2) our iterative weighting is superior to traditional TF-IDF, (3) the highest-weight priority without centroid preference and unit redundancy consideration helps improving summary quality, and (4) post-selection weight recalculation tends to raise summarization performance under some certain circumstances

Generating Aspect-oriented Multi-document Summarization with Event-Aspect Model

In this paper, we propose a novel approach to automatic generation of aspect-oriented summaries from multiple documents. We first develop an event-aspect LDA model to cluster sentences into aspects. We then use extended LexRank algorithm to rank the sentences in each cluster. We use Integer Linear Programming for sentence selection. Key features of our method include automatic grouping of semantically related sentences and sentence ranking based on extension of random walk model. Also, we implement a new sentence compression algorithm which use dependency tree instead of parser tree. We compare our method with four baseline methods. Quantitative evaluation based on Rouge metric demonstrates the effectiveness and advantages of our method.