Twitter Event Summarization Using Phrase Reinforcement Algorithm and NLP Features

Abstract-Now a day’s social networking sites are the fastest medium which delivers news to user as compare to the news paper and television. There so many social networking sites are present and one of them is Twitter. Twitter allows large no. of users to share/post their views, ideas on any particular event. According to recent survey daily 340 million Tweets are sent on Twitter which is on a different topic and only 4% of posts on Twitter have relevant news data. It is not possible for any human to read the posts to get meaningful information related to specific event. There is one solution to this problem i.e. we have to apply Summarization technique on it. In this paper we have used an algorithm which uses frequency count technique along with this we have also used some NLP features to summarize event specified by user. This automatic summarization algorithm handles the numerous, short, dissimilar, and noisy nature of tweets. We believe our novel approach helps users as well as researchers. DOI: 10.17762/ijritcc2321-8169.15020

A survey on opinion summarization technique s for social media

The volume of data on the social media is huge and even keeps increasing. The need for efficient processing of this extensive information resulted in increasing research interest in knowledge engineering tasks such as Opinion Summarization. This survey shows the current opinion summarization challenges for social media, then the necessary pre-summarization steps like preprocessing, features extraction, noise elimination, and handling of synonym features. Next, it covers the various approaches used in opinion summarization like Visualization, Abstractive, Aspect based, Query-focused, Real Time, Update Summarization, and highlight other Opinion Summarization approaches such as Contrastive, Concept-based, Community Detection, Domain Specific, Bilingual, Social Bookmarking, and Social Media Sampling. It covers the different datasets used in opinion summarization and future work suggested in each technique. Finally, it provides different ways for evaluating opinion summarization

Survey on Evaluation Methods for Dialogue Systems

In this paper we survey the methods and concepts developed for the evaluation of dialogue systems. Evaluation is a crucial part during the development process. Often, dialogue systems are evaluated by means of human evaluations and questionnaires. However, this tends to be very cost and time intensive. Thus, much work has been put into finding methods, which allow to reduce the involvement of human labour. In this survey, we present the main concepts and methods. For this, we differentiate between the various classes of dialogue systems (task-oriented dialogue systems, conversational dialogue systems, and question-answering dialogue systems). We cover each class by introducing the main technologies developed for the dialogue systems and then by presenting the evaluation methods regarding this class

Summarizing Dialogic Arguments from Social Media

Online argumentative dialog is a rich source of information on popular beliefs and opinions that could be useful to companies as well as governmental or public policy agencies. Compact, easy to read, summaries of these dialogues would thus be highly valuable. A priori, it is not even clear what form such a summary should take. Previous work on summarization has primarily focused on summarizing written texts, where the notion of an abstract of the text is well defined. We collect gold standard training data consisting of five human summaries for each of 161 dialogues on the topics of Gay Marriage, Gun Control and Abortion. We present several different computational models aimed at identifying segments of the dialogues whose content should be used for the summary, using linguistic features and Word2vec features with both SVMs and Bidirectional LSTMs. We show that we can identify the most important arguments by using the dialog context with a best F-measure of 0.74 for gun control, 0.71 for gay marriage, and 0.67 for abortion.Comment: Proceedings of the 21th Workshop on the Semantics and Pragmatics of Dialogue (SemDial 2017

V-ROOM: a virtual meeting system with intelligent structured summarisation

With the growth of virtual organisations and multinational companies, virtual collaboration tasks are becoming more important for employees. This paper describes the development of a virtual meeting system, called V-ROOM. An exploration of facilities required in such a system has been conducted. The findings highlighted that intelligent systems are needed, especially since information that individuals have to know and process, is vast. The survey results showed that meeting summarisation is one of the most important new features that should be added to virtual meeting systems for enterprises. This paper highlights the innovative methods employed in V-ROOM to produce relevant meeting summaries. V- ROOM's approach is compared to other methods from the literature and it is shown how the use of meta-data provided by parts of the V-ROOM system can improve the quality of summaries produced

"How May I Help You?": Modeling Twitter Customer Service Conversations Using Fine-Grained Dialogue Acts

Given the increasing popularity of customer service dialogue on Twitter, analysis of conversation data is essential to understand trends in customer and agent behavior for the purpose of automating customer service interactions. In this work, we develop a novel taxonomy of fine-grained "dialogue acts" frequently observed in customer service, showcasing acts that are more suited to the domain than the more generic existing taxonomies. Using a sequential SVM-HMM model, we model conversation flow, predicting the dialogue act of a given turn in real-time. We characterize differences between customer and agent behavior in Twitter customer service conversations, and investigate the effect of testing our system on different customer service industries. Finally, we use a data-driven approach to predict important conversation outcomes: customer satisfaction, customer frustration, and overall problem resolution. We show that the type and location of certain dialogue acts in a conversation have a significant effect on the probability of desirable and undesirable outcomes, and present actionable rules based on our findings. The patterns and rules we derive can be used as guidelines for outcome-driven automated customer service platforms.Comment: 13 pages, 6 figures, IUI 201