This is the python program which performs text summarization with pronoun replacement method. This method initially identifies pronouns in the text and replaces them with the nearest proper noun. With the help of replacement, the frequency of proper nouns will increase thereby improving the quality of the summarization.
The input text file is read and initial preprocessing is carried out such as tokenization and removing stop words. Then each word is tagged using nltk tagger.
The pronouns are placeholders for proper nouns. The words which are tagged as pronouns are replaced by a nearest proper noun. This replacement increases the frequency count of the proper noun.
Weight computing for words and sentences
Based on the frequency of words the weights are computed. The weightage for a sentence is the summation of weightage each word present in that sentence.
All the sentences are assigned a priority value depending on their weightage. The summary is formed based on the user-specified ratio by extracting higher priority sentences from the original text.
This method and results are published in papers:
Cite this work
Please cite as
Siddhaling Urolagin, Jagadish Nayak, Likitha Satish, “A Method to Generate Text Summary by Accounting Pronoun Frequency for Keywords Weightage Computation”, in IEEE, Scopus Indexed, The International Conference on Engineering and Technology, Turkey August 21-23, 2017.
Siddhaling Urolagin, Likitha Satish “Improving the Quality of Text Summarization using Pronoun Replacement Technique”, in IEEE International conference on Recent Trends in Electronics Information and Communication Technology, Bangalore, pp. 1991-1995, India, 2017.
Further Projects and Contact
For further reading and other projects please visit