rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Automatic Hausa text summarization based on feature extraction using naive Bayes model
  name:
    name part: Bashir, Muazzam
    role:
      role term: creator
    role:
      role term: author
  name:
    name part: Universiti Sultan Zainal Abidin
    name part: Faculty of Informatics and Computing
  type of resource: text
  genre: Dissertations, Academic
  origin info:
    place:
      place term: y
    date issued: 2016
    issuance: monographic
  language:
    language term: ng
  physical description:
    form: print
    extent: 111leaves ; 30 cm.
  abstract: As a result of advances in information technology, information overload becomes a global problem. .Automatic text summarization, a branch of natural language processing, is one of the techniques that can be used to overcome the challenge. Automatic text summarization is a technique used to summarize a text without losing the essential information. Although there are many commercial text summarization tools available online, there is a limited research to summarize text automatically in Rausa. This study was conducted to develop a system to summarize text automatically in Rausa language. Rausa, a Chadic language that is widely spoken in West Africa, is a low resource language. A data set of 10 Rausa documents were extracted from two different newspapers which are 'Aminiya' and 'Leadership Rausa'. Each document was given to three linguistic experts for human made summary. The study adopted five features (keyword, length, title, cue phrases and location of a sentence) in the summarization process. Rausa morphological rules were reviewed, while Porter's algorithm was modified to fit the language. Meanwhile, a stemming algorithm was developed to stem Rausa terms. Term Frequency Inverse Sentence Frequency and Kmixture Probabilistic models were used to weigh each word before and after stemming. A set of words was chosen as keywords based on a threshold value. The keywords were used to produce summaries based on the models. This is to determine the fitness of the models and the impact of stemming on automatic text summarization for the Rausa language. Moreover, Naive Bayes model was employed to weigh each sentence based on its features. The system has produced a set of summary of sentences based on the threshold value. Considering human made summaries are perfect, the researcher has compared the system generated output to human made summaries. The results show that, the Term Frequency Inverse Sentence Frequency model, having an average F-score of 56.0% outclasses K-mixture …
  table of contents: 1. Introduction -- 2. Literature review -- 3. Methodology -- 4. Results and discussion -- 5. Conclusion
  note: Muazzam Bashir
  note: Thesis (Degree of Master of Science in the Faculty of Informatics and Computing) - Universiti Sultan Zainal Abidin, 2016
  note: Includes bibliographical references (leaves 90-96)
  subject:
    name:
      name part: Universiti Sultan Zainal Abidin
    topic: Dissertations
  subject:
    name:
      name part: Universiti Sultan Zainal Abidin
    topic: Faculty of Informatics and Computing
    genre: Dissertations
  subject:
    topic: Automatic abstracting
  subject:
    topic: Hausa Language
  classification: P98.5.A87 B37 2016
  record info:
    record content source: UniSZA
    record creation date: 160811
    record change date: 20230112093000.0
    record identifier: 0000089934