Summarization of Films and Documentaries Based on Subtitles and Scripts

We assess the performance of generic text summarization algorithms applied to films and documentaries, using the well-known behavior of summarization of news articles as reference. We use three datasets: (i) news articles, (ii) film scripts and subtitles, and (iii) documentary subtitles. Standard ROUGE metrics are used for comparing generated summaries against news abstracts, plot summaries, and synopses. We show that the best performing algorithms are LSA, for news articles and documentaries, and LexRank and Support Sets, for films. Despite the different nature of films and documentaries, their relative behavior is in accordance with that obtained for news articles.Comment: 7 pages, 9 tables, 4 figures, submitted to Pattern Recognition Letters (Elsevier

Summarization of documentaries

Video summarization algorithms present condensed versions of a full length video by identifying the most significant parts of the video. In this paper, we propose an automatic video summarization method using the subtitles of videos and text summarization techniques. We identify significant sentences in the subtitles of a video by using text summarization techniques and then we compose a video summary by finding the video parts corresponding to these summary sentences. © 2011 Springer Science+Business Media B.V

Co-Regularized Deep Representations for Video Summarization

Compact keyframe-based video summaries are a popular way of generating viewership on video sharing platforms. Yet, creating relevant and compelling summaries for arbitrarily long videos with a small number of keyframes is a challenging task. We propose a comprehensive keyframe-based summarization framework combining deep convolutional neural networks and restricted Boltzmann machines. An original co-regularization scheme is used to discover meaningful subject-scene associations. The resulting multimodal representations are then used to select highly-relevant keyframes. A comprehensive user study is conducted comparing our proposed method to a variety of schemes, including the summarization currently in use by one of the most popular video sharing websites. The results show that our method consistently outperforms the baseline schemes for any given amount of keyframes both in terms of attractiveness and informativeness. The lead is even more significant for smaller summaries.Comment: Video summarization, deep convolutional neural networks, co-regularized restricted Boltzmann machine

Classification of dual language audio-visual content: Introduction to the VideoCLEF 2008 pilot benchmark evaluation task

VideoCLEF is a new track for the CLEF 2008 campaign. This track aims to develop and evaluate tasks in analyzing multilingual video content. A pilot of a Vid2RSS task involving assigning thematic class labels to video kicks off the VideoCLEF track in 2008. Task participants deliver classification results in the form of a series of feeds, one for each thematic class. The data for the task are dual language television documentaries. Dutch is the dominant language and English-language content (mostly interviews) is embedded. Participants are provided with speech recognition transcripts of the data in both Dutch and English, and also with metadata generated by archivists. In addition to the classification task, participants can choose to participate in a translation task (translating the feed into a language of their choice) and a keyframe selection task (choosing a semantically appropriate keyframe for depiction of the videos in the feed)

TRECVID 2008 - goals, tasks, data, evaluation mechanisms and metrics

The TREC Video Retrieval Evaluation (TRECVID) 2008 is a TREC-style video analysis and retrieval evaluation, the goal of which remains to promote progress in content-based exploitation of digital video via open, metrics-based evaluation. Over the last 7 years this effort has yielded a better understanding of how systems can effectively accomplish such processing and how one can reliably benchmark their performance. In 2008, 77 teams (see Table 1) from various research organizations --- 24 from Asia, 39 from Europe, 13 from North America, and 1 from Australia --- participated in one or more of five tasks: high-level feature extraction, search (fully automatic, manually assisted, or interactive), pre-production video (rushes) summarization, copy detection, or surveillance event detection. The copy detection and surveillance event detection tasks are being run for the first time in TRECVID. This paper presents an overview of TRECVid in 2008

Access to recorded interviews: A research agenda

Recorded interviews form a rich basis for scholarly inquiry. Examples include oral histories, community memory projects, and interviews conducted for broadcast media. Emerging technologies offer the potential to radically transform the way in which recorded interviews are made accessible, but this vision will demand substantial investments from a broad range of research communities. This article reviews the present state of practice for making recorded interviews available and the state-of-the-art for key component technologies. A large number of important research issues are identified, and from that set of issues, a coherent research agenda is proposed

Automatic categorization and summarization of documentaries

In this paper, we propose automatic categorization and summarization of documentaries using subtitles of videos. We propose two methods for video categorization. The first makes unsupervised categorization by applying natural language processing techniques on video subtitles and uses the WordNet lexical database and WordNet domains. The second has the same extraction steps but uses a learning module to categorize. Experiments with documentary videos give promising results in discovering the correct categories of videos. We also propose a video summarization method using the subtitles of videos and text summarization techniques. Significant sentences in the subtitles of a video are identified using these techniques and a video summary is then composed by finding the video parts corresponding to these summary sentences. © 2010 The Author(s)