Performance Evaluation

Compression Ratio

The Compression Ratio (CR) is the ratio of size of the summarized text document to the total size of the original text documents.
CR = |d|/|D|
Where |d| represents the size of the summarized text is document and |D| is the total size of the original text collection. Rouge Score: It is used for evaluating the summarization.

ROUGE

ROUGE stands for Recall-Oriented Understudy for Gisting Evaluation, it includes several measures to quantitatively compare system-generated to human-generated summaries, counting the number of overlapping n-grams of various lengths, word pairs and word sequences between the summaries. In this work the average precision, recall and F-measure scores generated by ROUGE-1, ROUGE-2, and ROUGE-L are used to measure the performance of the summaries The performance parameters of proposed summarizers i.e. compression ratio, ROUGE are evaluated for three different scenarios:
The summarizers are evaluated for the following three cases:

Case 1: Summarization without performing clustering and semantic similarity.
Case 2: Summarization with clustering but without considering semantic similarity.
Case 3: Summarization by considering both clustering and semantic similarity.
Results of Compression Ratio
It is apparent from the bubble graph below that considering the semantic similarity (Case 3) will definitely give better results for generating effective and meaningful summary of text document collections. These results clearly indicates that semantic similarity along with the clustering gives better summarization results as compared to the summarization without semantic similarity and clustering.
Results of ROUGE Score
As expected from the results, ROUGE scores are found higher for the case III than the other two cases. Case III consider both the textual similarity (using clustering) and semantic similarity which makes sure that best summarization content units participate in the summary generation. Case II gives better results than the Case I results, in other words summarization using clustering gives better summarization results as compared to the summarization performed without performing clustering. It indicates that summarization performed on the clustered text documents is more accurate since similar text information is grouped within the same clusters.

ROUGE 1

ROUGE 2