Abstract. Timeline summaries are an effective way for helping newspaper readers to keep track of long-lasting news stories, such as the Egypt revolution. A good timeline summary provides a concise description of only the main events, while maintaining good understandability. As manual construction of timelines is very time-consuming, there is a need for automatic approaches. However, automatic selection of relevant events is challenging due to the large amount of news articles published every day. Furthermore, current state-of-the-art systems produce summaries that are suboptimal in terms of relevance and understandability. We present a new approach that exploits the headlines of online news articles instead of the articles' full text. The quantitative and qualitative results from our user studies confirm that our method outperforms state-of-the-art system in these aspects.

No abstract

Word segmentation is one of the most important tasks in NLP. This task, within Vietnamese language and its own features, faces some challenges, especially in words boundary determination. To tackle the task of Vietnamese word segmentation, in this paper, we propose the WS4VN system that uses a new approach based on Maximum matching algorithm combining with stochastic models using part-ofspeech information. The approach can resolve word ambiguity and choose the best segmentation for each input sentence. Our system gives a promising result with an F-measure of 97%, higher than the results of existing publicly available Vietnamese word segmentation systems.

Automatic timeline summarization (TLS) generates precise, dated overviews over (often prolonged) events, such as wars or economic crises. One subtask of TLS selects the most important dates for an event within a certain time frame. Date selection has up to now been handled via supervised machine learning approaches that estimate the importance of each date separately, using features such as the frequency of date mentions in news corpora. This approach neglects interactions between different dates that occur due to connections between subevents. We therefore suggest a joint graphical model for date selection. Even unsupervised versions of this model perform as well as supervised state-of-theart approaches. With parameter tuning on training data, it outperforms prior supervised models by a considerable margin.