Computational Techniques for Text Summarization based on Cognitive Intelligence

Appendix B: Solutions to Selected Exercises

Chapter 1

Ex 2:

	Doc1	Doc2	Doc3
Car	44.55	6.6	39.6
Auto	6.24	68.64	0
Insurance	0	53.46	46.98
Best	21	0	25.5

Ex 3:

• A tf = 3/3; idf = log(10000/50) = 5.3; tf-idf = 5.3
• B tf = 2/3; idf = log(10000/1300) = 2.0; tf-idf = 1.3
• C tf = 1/3; idf = log(10000/250) = 3.7; tf-idf=1.2

Ex 4:

1. (i) Each occurring term has to be assigned a unique number that corresponds to a dimension of the term space. This can be done in several different ways. For example, one could simply take the ordinal of the term in the alphabet or the order of appearance. Using the latter approach, the document vectors with term frequency are presented as follows:

• dimensions ...