Xingbang Liu

2papers

2 Papers

CLOct 16, 2019
Automated Text Summarization for the Enhancement of Public Services

Xingbang Liu, Janyl Jumadinova

Natural language processing and machine learning algorithms have been shown to be effective in a variety of applications. In this work, we contribute to the area of AI adoption in the public sector. We present an automated system that was used to process textual information, generate important keywords, and automatically summarize key elements of the Meadville community statements. We also describe the process of collaboration with My Meadville administrators during the development of our system. My Meadville, a community initiative, supported by the city of Meadville conducted a large number of interviews with the residents of Meadville during the community events and transcribed these interviews into textual data files. Their goal was to uncover the issues of importance to the Meadville residents in an attempt to enhance public services. Our AI system cleans and pre-processes the interview data, then using machine learning algorithms it finds important keywords and key excerpts from each interview. It also provides searching functionality to find excerpts from relevant interviews based on specific keywords. Our automated system allowed the city to save over 300 hours of human labor that would have taken to read all interviews and highlight important points. Our findings are being used by My Meadville initiative to locate important information from the collected data set for ongoing community enhancement projects, to highlight relevant community assets, and to assist in identifying the steps to be taken based on the concerns and areas of improvement identified by the community members.

IRFeb 21, 2018
Investigating Rumor News Using Agreement-Aware Search

Jingbo Shang, Tianhang Sun, Jiaming Shen et al.

Recent years have witnessed a widespread increase of rumor news generated by humans and machines. Therefore, tools for investigating rumor news have become an urgent necessity. One useful function of such tools is to see ways a specific topic or event is represented by presenting different points of view from multiple sources. In this paper, we propose Maester, a novel agreement-aware search framework for investigating rumor news. Given an investigative question, Maester will retrieve related articles to that question, assign and display top articles from agree, disagree, and discuss categories to users. Splitting the results into these three categories provides the user a holistic view towards the investigative question. We build Maester based on the following two key observations: (1) relatedness can commonly be determined by keywords and entities occurring in both questions and articles, and (2) the level of agreement between the investigative question and the related news article can often be decided by a few key sentences. Accordingly, we use gradient boosting tree models with keyword/entity matching features for relatedness detection, and leverage recurrent neural network to infer the level of agreement. Our experiments on the Fake News Challenge (FNC) dataset demonstrate up to an order of magnitude improvement of Maester over the original FNC winning solution, for agreement-aware search.