Browsing by Subject "Summarization"

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    An answer recommendation framework for an online cancer community forum
    (Springer Nature, 2023-05-15) Athira, B.; Idicula, Sumam Mary; Jones, Josette; Kulanthaivel, Anand; BioHealth Informatics, School of Informatics and Computing
    Health community forums are a kind of online platform to discuss various matters related to management of illness. People are increasingly searching for answers online, particularly when they are diagnosed with cancer like life-threatening diseases. People seek suggestions or advice through these platforms to make decisions during their treatments. However, locating the correct information or similar people is often a great challenge for them. In this scenario, this paper proposes an answer recommendation system in an online breast cancer community forum that provide guidance and valuable references to users while making decisions. The answer is the summary of already discussed topic in the forum, so that they do not need to go through all the answer posts which spans over multiple pages or initiate a thread once again. There are three phases for the answer recommendation system, including query similarity model to retrieve the past similar query, query-answer pair generation and answer recommendation. Query similarity model is employed by a Siamese network with Bi-LSTM architecture which could achieve an F1-score of 85.5%. Also, the paper shows the efficacy of transfer learning technique to generalize the model well in our breast cancer query-query pair data set. The query-answer pairs are generated by an extractive summarization technique that is based on an optimization algorithm. The effectiveness of the generated summary is evaluated based on a manually generated summary, and the result shows a ROUGE-1 score of 49%.
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    Temporal profile summarization and indexing for surveillance videos
    (2014-12) Bagheri, Saeid; Zheng, Jiangyu; Fang, Shiaofen; Tuceryan, Mihran
    Surveillance videos are recorded continually and the retrieval of such videos currently still relies on human operators. Automatic retrieval has not reached a satisfactory accuracy. As an intermediate representation, this work develops multiple original temporal profiles of video to convey accurate temporal information in the video while keeping certain spatial characteristics. These are effective methods to visualizes surveillance video contents efficiently in a 2D temporal image, suitable for indexing and retrieving a large video database. We are aiming to provide a compact index that is intuitive and preserves most of the information in the video in order to avoid browsing extensive video clips frame by frame. By considering some of the properties of static surveillance videos, we aim at accentuating the temporal dimension in our visualization. We have introduced our framework as three unique methods that visualize different aspects of a surveillance video, plus an extension to non-static surveillance videos. In our first method "Localized Temporal Profile", by knowing that most surveillance videos are monitoring specific locations, we try to emphasize the other dimension, time, in our solution. we focus on describing all the events only in critical locations of the video. In our next method "Multi-Position Temporal Profile", we generate an all-inclusive profile that covers all the events in the video field of view. In our last method "Motion Temporal Profile" we perform in-depth analysis of scene motion and try to handle targets with non-uniform, non-translational motion in our temporal profile. We then further extend our framework by loosening the constraint that the video is static and including cameras with smooth panning motion as such videos are widely used in practice. By performing motion analysis on the camera, we stabilize the camera to create a panorama-like effect for the video, allowing us to utilize all of the aforementioned methods. The resulting profiles allows temporal indexing to each video frame, and contains all spatial information in a continuous manner. It also shows the actions and progress of events in the temporal profile. Flexible browsing and effective manipulation of videos can be achieved using the resulting video profiles.