Front-page news selection is the task of finding important news articles in news aggregators. In this study, we examine news selection for public front pages using raw text, without any meta-attributes such as click counts. A novel algorithm is introduced by jointly considering the importance and diversity of selected news articles and the length of front pages. We estimate the importance of ne...

In this paper we investigate a new task of automatically constructing an overview article from a given set of news articles about a news event. We propose a news synthesis approach to address this task based on passage segmentation, ranking, selection and merging. Our proposed approach is compared with several typical multi-document summarization methods on the Wikinews dataset, and achieves th...

Content analysis of news stories (whether manual or automatic) is a cornerstone of the communication studies field. However, much research is conducted at the level of individual news articles, despite the fact that news events (especially significant ones) are frequently presented as “stories” by news outlets: chains of connected articles covering the same event from different angles. These st...

This paper presents a visual analytics approach to exploring large news articles collection in the domains of polarity, spatial and entity analysis. The exploration is performed on the data collected with Europe Media Monitor (EMM), a system which monitors over 2,500 online sources and processes 90,000 articles per day. In the analysis of the news feeds, we want to find out which topics are imp...

Emerging within Systemic Linguistics, Appraisal/Evaluation is a framework for analyzing the language of evaluation, providing techniques for the systematic analysis of evaluation and stance as they operate in whole texts and in groupings of texts. There are three systems in the Appraisal framework: Attitude, Engagement, and Graduation. This study sets out to analyze the use of the system of Eng...

Many Web news portals have provided clustered news categories for readers to browse many related news articles. However, to the best of our knowledge, they only provide monolingual services. For readers who want to find related news articles in different languages, the search process is very cumbersome. In this paper, we propose a cross-lingual news group recommendation framework using the cros...

Prior research has reported evidence of polarization in the consumption and distribution of political news in online social media. Methods to effectively measure slant of news articles at a large scale are lacking in prior literature. To fill this gap, we propose a method to measure slant of individual news articles by observing their sharing patterns on Twitter. Our method monitors tweets abou...

To support the efficient gathering of diverse information about a news event, we focus on descriptions of named entities (persons, organizations, locations) in news articles. We extend the stakeholder mining proposed by Ogawa et al. and extract descriptions of named entities in articles. We propose three measures (difference in opinion, difference in details, and difference in factor coverage) ...

Tens of thousands of news articles are posted on-line each day, covering topics from politics to science to current events. In order to better cope with this overwhelming volume of information, RSS (news) feeds are used to categorize newly posted articles. Nonetheless, most RSS users must filter through many articles within the same or different RSS feeds in order to locate articles pertaining ...

We perform a study on cold-start news popularity prediction using a collection of 13,319 news articles obtained from Yahoo News. We characterise the online popularity of news articles by two different metrics and try to predict them using machine learning techniques. Contrary to a prior work on the same topic, our findings indicate that predicting the news popularity at cold start is a difficul...