Improving chronological ordering of sentences extracted from multiple newspaper articles

Bidirectional Sentence Ordering with Interactive Decoding

Sentence ordering aims at restoring orders of shuffled sentences in a paragraph. Previous methods usually predict orders in a single direction, i.e., from head to tail. However, unidirectional prediction inevitably causes error accumulation, which restricts performance. In this paper, we propose a bidirectional ordering method, which predicts orders in both head-to-tail and tail-to-head directions at the same time. In our bidirectional ordering method, two directions can interact with each other and help alleviate the error accumulation problem of ordering. Experiments demonstrate that our method can effectively improve performance of previous models.

An EDU-Based Approach for Thai Multi-Document Summarization and Its Application

Due to lack of a word/phrase/sentence boundary, summarization of Thai multiple documents has several challenges in unit segmentation, unit selection, duplication elimination, and evaluation dataset construction. In this article, we introduce Thai Elementary Discourse Units (TEDUs) and their derivatives, called Combined TEDUs (CTEDUs), and then present our three-stage method of Thai multi-document summarization, that is, unit segmentation, unit-graph formulation, and unit selection and summary generation. To examine performance of our proposed method, a number of experiments are conducted using 50 sets of Thai news articles with their manually constructed reference summaries. Based on measures of ROUGE-1, ROUGE-2, and ROUGE-SU4, the experimental results show that: (1) the TEDU-based summarization outperforms paragraph-based summarization; (2) our proposed graph-based TEDU weighting with importance-based selection achieves the best performance; and (3) unit duplication consideration and weight recalculation help improve summary quality.