# Automatic Text Summarization for Hindi Using Real Coded Genetic Algorithm
## 4 Experimental Setup
### Results and Discussion
#### 4.3.2 Summary of Compression Rates  
In this research, various feature combinations are experimented over the HHD test dataset. The information on the best feature sets is discussed for different compression rates of 25%, 50%, and 65% (Tables 7-9), respectively. Table 7 discusses the information on the best 14 feature sets (Set1 to Set 14) at 25% compression rate. For Set1, \(F_{sp}\) alone gives F-measure as 0.52. For Set2, addition of \(F_{nd}\) increases F-measure to 0.58. For Set3, adding \(F_{ls}\) to \(F_{sp}\) and \(F_{nd}\) reduces the F-measure to 0.55 because of the lesser feature weight of \(F_{ls}\). For Set4, involving \(F_{kw}\) to \(F_{sp}\) and \(F_{nd}\) increases F-measure to 0.60. For Set5, addition of \(F_{ss}\) to \(F_{sp}\) and \(F_{kw}\) increases F-measure to 0.64%. For Set6, consideration of the first four features \(F_{sp}\), \(F_{nd}\), \(F_{ls}\), and \(F_{kw}\) unalters the F-measure to 0.64. For Set7, two high-weighted features \(F_{ss}\) and \(F_{ne}\) increase the F-measure to 0.68. For Set8, inclusion of \(F_{nd}\), \(F_{kw}\), \(F_{ss}\), and \(F_{ne}\) further increases the F-measure by 0.72 since most of these features are high weighted features. For Set9, adding \(F_{ls}\) to \(F_{sp}\), \(F_{kw}\), \(F_{ss}\), and \(F_{ne}\) again reduces the F-measure to 0.63. For Set10, consideration of \(F_{kw}\), \(F_{ss}\), \(F_{ne}\), and \(F_{dh}\) makes the F-measure 0.75. However, for Set11, the first five features and seventh feature  
Figure 3: Feature weights for different features. (\(F_{sp}\), \(F_{nd}\), \(F_{ls}\), \(F_{kup}\), \(F_{ss}\), and \(F_{eh}\)) again reduces the F-measure to 0.65 since some features are of lesser weights. For Set12, \(F_{nd}\), \(F_{kup}\), \(F_{ss}\), \(F_{eh}\), and \(F_{hi}\) again increase F-measure as 0.75 since all other features except \(F_{nd}\) are high weighted features. For Set13, including \(F_{sp}\), \(F_{kup}\), \(F_{ss}\), \(F_{ne}\), \(F_{dh}\), and \(F_{hi}\) further increases the F-measure to 0.78. For Set14, all eight features do not make any difference in the F-measure. Hence, Set13 and Set14 feature sets provide better results. Table 8 discusses the information on the best 14 feature sets (Set1 to Set 14) at 50% compression rate. For Set1 and Set2, F-measure increases from 0.61 to 0.64. For Set3, adding \(F_{ls}\) to \(F_{sp}\) and \(F_{hd}\) reduces F-measure to 0.62 because of the lesser feature weight of \(F_{ls}\). For Set4 to Set6, the F-measure increases from 0.65 to 0.68. For Set7, Set8, and Set10, high-weighted features \(F_{ss}\) and \(F_{ne}\) increase the F-measure to 0.71, 0.72, and 0.74, respectively. However, for Set9 and Set11, \(F_{ls}\) reduces the F-measure to 0.63 and 0.65. For Set12 and Set13, high-weighted features again increase the F-measure from 0.77 to 0.81. For Set14, all the features are incorporated to yield an F-measure of 0.83. Table 9 discusses the information on the best 14 feature sets (Set1 to Set 14) at 65% compression rate. For Set1 and Set2, F-measure increases from 0.65 to 0.71. For Set3, adding \(F_{ls}\) to \(F_{sp}\) and \(F_{hd}\) reduces the F-measure to 0.68 because of the lesser feature weight of \(F_{ls}\). For Set4 to Set8, F-measure increases from 0.75 to 0.84 because of high-weighted features. However, for Set9 and Set11, the F-measure reduces to 0.75 and 0.80, respectively, because of \(F_{ls}\). For Set12 and Set13, high-weighted features increase the F-measure from 0.82 to 0.86. For Set14, all the features are incorporated to yield an F-measure of 0.87.