Multi-document summarization

About: Multi-document summarization is a research topic. Over the lifetime, 2270 publications have been published within this topic receiving 71850 citations.

Weighted summarization of Student Feedback using Sentiment Analysis

Abstract: Every year massive amount of feedback is gathered from students regarding subjects and its respective faculty. The amount of time to analyze this data manually is a very tedious and time consuming. This is where the summarization feature comes into picture. It extracts important information found in every feedback document. Automatic summarization based on word frequency statistics takes comments and weights them to produce word frequency and then sentence frequency. Also, the sentiment information in these documents belongs to a wide spectrum ranging from positive to negative. SentiWordNet assigns sentiment numerical scores: positive or negative. Thus, providing clues for sentiment analysis. The spell-checker helps to rectify the incorrect words for proper implementation of those two concepts.

An Adapted Entity Summarization Service for an Enhancement Video System

Abstract: Knowledge bases’ field has known a huge development these last years. It represents the interest of several types of systems, such as intelligent systems, search engines and several other applications. In some applications, this type of data becomes unsuitable because of the large amount of information it contains. From this problem has emerged the appearance of entity summarization methods.

Semantic Multi-document Update Summarization Techniques

Abstract: As huge amounts of knowledge are created rapidly, effective information access becomes an important issue. Especially for critical domains, such as medical and financial areas, efficient retrieval of concise and relevant information is highly desired. In this paper we propose two new multi-document summarization techniques that make use of WordNet, a general knowledge source from Princeton University. We participated in the Text Analysis Conference 2008 update summarization task and ranked in the middle tier of about 70 systems.

From video summarization to real time video summarization in smart cities and beyond: A survey

Abstract: With the massive expansion of videos on the internet, searching through millions of them has become quite challenging. Smartphones, recording devices, and file sharing are all examples of ways to capture massive amounts of real time video. In smart cities, there are many surveillance cameras, which has created a massive volume of video data whose indexing, retrieval, and administration is a difficult problem. Exploring such results takes time and degrades the user experience. In this case, video summarization is extremely useful. Video summarization allows for the efficient storing, retrieval, and browsing of huge amounts of information from video without sacrificing key features. This article presents a classification and analysis of video summarization approaches, with a focus on real-time video summarization (RVS) domain techniques that can be used to summarize videos. The current study will be useful in integrating essential research findings and data for quick reference, laying the preliminaries, and investigating prospective research directions. A variety of practical uses, including aberrant detection in a video surveillance system, have made successful use of video summarization in smart cities.