- Link:
- http://cscjournals.org/csc/manuscript/Journals/IJCSS/Volume1/Issue4/IJCSS-20.pdf
- Collection:
-
- Subjects
- fuzzy sets mass assignment asymmetric word similarity topic similarity summarization
- Creators:
- Trevor Martin Masrah Azrifah Azmi Murad
- Source
- International Journal of Computer Science and Security
- Publisher
- Computer Science Journals
- Description
- Information is increasing every day and thousands
of documents are produced and made available in the Internet. The
amount of information available in documents exceeds our capacity
to read them. We need access to the right information without
having to go through the whole document. Therefore, documents need
to be compressed and produce an overview so that these documents
can be utilized effectively. Thus, we propose a similarity model
with topic similarity using fuzzy sets and probability theories to
extract the most representative sentences. Sentences with high
weights are extracted to form a summary. On average, our model
(known as MySum) produces summaries that are 60% similar to the
manually created summaries, while tf.isf algorithm produces
summaries that are 30% similar. Two human summarizers, named P1 and
P2, produce summaries that are 70% similar to each other using
similar sets of documents obtained from TREC.
- Source
- International Journal of Computer Science and
Security
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