Compact representations of video, or video summaries, data greatly enhances efficient video browsing. However, rigorous evaluation of video summaries generated by automatic summarization systems is a complicated process. In this paper we examine the summary evaluation problem. Text summarization is the oldest and most successful summarization domain. We show some parallels between these to doma...

Microblogging sites, such as Twitter, have become increasingly popular in recent years for reporting details of real world events via the Web. Smartphone apps enable people to communicate with a global audience to express their opinion and commentate on ongoing situations often while geographically proximal to the event. Due to the heterogeneity and scale of the data and the fact that some mess...

these are discussed in Section 3). Segments with strong Text summarization may soon become a competitive method of answering queries asked of large text corpora. A query brings up a set of documents. These documents are then filtered by a summarizer: it constructs brief summaries from document fragments conceptually close to the query terms. We present the implementation of such a summarization...

A serious bottleneck in the development of trainable text summarization systems is the shortage of training data. Constructing such data is a very tedious task, especially because there are in general many different correct ways to summarize a text. Fortunately we can utilize the Internet as a source of suitable training data. In this paper, we present a summarization system that uses the web a...

In this demo, we present SciSumm, an interactive multi-document summarization system for scientific articles. The document collection to be summarized is a list of papers cited together within the same source article, otherwise known as a co-citation. At the heart of the approach is a topic based clustering of fragments extracted from each article based on queries generated from the context sur...

News production, delivery, and consumption are increasing in ubiquity and speed, spreading over more software and hardware platforms, in particular mobile devices. This has led to an increasing interest in automated methods for multi-document summarization. We start this chapter with discussing several new alternatives for automated news summarization, with a particular focus on temporal text m...

Research in multi-document summarization has focused on newswire corpora since the early beginnings. However, the newswire genre provides genre-specific features such as sentence position which are easy to exploit in summarization systems. Such easy to exploit genre-specific features are available in other genres as well. We therefore present the new hMDS corpus for multi-document summarization...

Huge amount of information is present in the World Wide Web and a large amount is being added to it frequently. A query-specific summary of multiple documents is very helpful to the user in this context. Currently, few systems have been proposed for query-specific, extractive multi-document summarization. If a summary is available for a set of documents on a given query and if a new document is...

Current graph-based approaches to automatic text summarization, such as LexRank and TextRank, assume a static graph which does not model how the input texts emerge. A suitable evolutionary text graph model may impart a better understanding of the texts and improve the summarization process. We propose a timestamped graph (TSG) model that is motivated by human writing and reading processes, and ...

This paper discusses a text extraction approach to multidocument summarization that builds on single-document summarization methods by using additional, available in-, formation about the document set as a whole and the relationships between the documents. Multi-document summarization differs from single in that the issues of compression, speed, redundancy and passage selection are critical in ...