Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

Browse

Subject: search.filters.subject.Text summarization

Search Results

Now showing 1 - 3 of 3
  • No Thumbnail Available
    Item
    A novel technique for efficient text document summarization as a service
    (2013) Bagalkotkar, A.; Khandelwal, A.; Pandey, S.; Kamath S․, S.S.
    Due to an exponential growth in the generation of web data, the need for tools and mechanisms for automatic summarization of Web documents has become very critical. Web data can be accessed from multiple sources, for e.g. on different Web pages, which makes searching for relevant pieces of information a difficult task. Therefore, an automatic summarizer is vital towards reducing human effort. Text summarization is an important activity in the analysis of a high volume text documents and is currently a major research topic in Natural Language Processing. It is the process of generation of the summary of an input document by extracting the representative sentences from it. In this paper, we present a novel technique for generating the summarization of domain-specific text from a single Web document by using statistical NLP techniques on the text in a reference corpus and on the web document. The summarizer proposed generates a summary based on the calculated Sentence Weight (SW), the rank of a sentence in the document's content, the number of terms and the number of words in a sentence, and using term frequency in the input corpus. © 2013 IEEE.
  • No Thumbnail Available
    Item
    Extractive Document Summarization Using a Supervised Learning Approach
    (Institute of Electrical and Electronics Engineers Inc., 2018) Charitha, S.; Chittaragi, N.B.; Koolagudi, S.G.
    In this paper, we present a model for extractive multi-document text summarization using a supervised learning approach. The model uses a convolutional neural networks (CNN) which is capable of learning sentence features on its own for sentence ranking. This approach has been used in order to avoid the overhead of extracting features from sentences manually. Integer linear programming (ILP) approach has been adopted for selecting sentences to generate the summary based on sentence ranks. This ILP model minimizes the redundancy in the generated summary. We have evaluated our proposed approach on the DUC 2007 dataset and its performance is found to be competitive or better in comparison with state-of-the-art systems. © 2018 IEEE.
  • No Thumbnail Available
    Item
    TORA: Text Summarization Using Optical Character Recognition and Attention Neural Networks
    (Springer Science and Business Media Deutschland GmbH, 2022) Sneha, H.R.; Annappa, B.
    Text Summarization is the process of creating a short and coherent version of a longer document that holds the same meaning as that of the original data. This article illustrates the technique to read the text in a printed document (such as newspaper, brochure, web document, etc.) and generate a summary of text. The method proposed is named Text Summarization using Optical Character Recognition and Attention Neural Networks (TORA). TORA can perform extractive summarization of a news article with the aid of Recurrent Neural Networks, Bidirectional Long Short-Term Memory, and Bahadanu Attention Network. The experimental results of the proposed method are promising. The experimental results have shown 80% accuracy in producing the summary from the large text document. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.