# Y-Rank: A Multi-Feature-Based Keyphrase Extraction Method for Short Text
## 6 Experiments
### Experimental Results and Analysis
#### 6.2.2 Statistical Score Importance  
This section demonstrates the effectiveness of \(T_{SenInfo}\) by performing ablation experiments of Y-Rank on different heuristic weighting rules, to make it the best optimization of the algorithm. Here, we also choose the wikinews-fr-100 dataset. \(T_{SenInfo}\) is positively correlated with the importance of keyphrases, which demonstrates the information of the word. If the sentence that contains the word is longer, it is more likely to be informative and relevant to the document's theme. Here, we heuristically expand (1) by trying different weight combinations, which depends on the modification of numerator and denominator. Furthermore, the division of \(T_{SenInfo}\) by \(T_{Rel}\) is also considered to penalize candidates that frequently occur but have many different terms in the context. The total seven strategies, which are classified into three categories and listed as follows.  
* Only the denominator modification \[S(t)=\frac{T_{\text{Rel}}\ast T_{\text{Position}}}{T_{\text{Case}}+\frac{T_{ \text{Norm}}}{T_{\text{Rel}}}+\frac{T_{\text{SenIntro}}}{T_{\text{Rel}}}+ \frac{T_{\text{SenInfo}}}{T_{\text{Rel}}}}\] (8) \[S(t)=\frac{T_{\text{Rel}}\ast T_{\text{Position}}}{T_{\text{Case}}+\frac{T_{ \text{Norm}}}{T_{\text{Rel}}}+\frac{T_{\text{SenIntro}}}{T_{\text{Rel}}}+T_{ \text{SenInfo}}}\] (9)
* Only the numerator modification \[S(t)=\frac{T_{\text{Rel}}\ast T_{\text{Position}}+\frac{T_{\text{SenInfo}}}{T_{ \text{Case}}}}{T_{\text{Case}}+\frac{T_{\text{Norm}}}{T_{\text{Rel}}}+ \frac{T_{\text{SenIntro}}}{T_{\text{Rel}}}}\] (10) \[S(t)=\frac{T_{\text{Rel}}\ast T_{\text{Position}}+T_{\text{SenInfo}}}{T_{ \text{Case}}+\frac{T_{\text{Norm}}}{T_{\text{Rel}}}+\frac{T_{\text{SenIntro}}} {T_{\text{Rel}}}}\] (11) \[S(t)=\frac{T_{\text{Rel}}\ast(T_{\text{Position}}+T_{\text{SenInfo}})}{T_{ \text{Case}}+\frac{T_{\text{Norm}}}{T_{\text{Rel}}}+\frac{T_{\text{SenIntro}}} {T_{\text{Rel}}}}\] (12) \[S(t)=\frac{T_{\text{Rel}}\ast T_{\text{Position}}\ast T_{\text{SenInfo}}}{T_{ \text{Case}}+\frac{T_{\text{Norm}}}{T_{\text{Rel}}}+\frac{T_{\text{SenIntro}}} {T_{\text{Rel}}}}\] (13)  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Strategy** & **P** & **R** & **F** & **MRR** & **MAP** & **Bpref** \\ \hline Original-strategy (Yakef) & 20.625 & 18.082 & 18.933 & 33.487 & 40.522 & 60.535 \\ Strategy 1 & 20.750 & 18.154 & 19.024 & 33.459 & 40.550 & 60.573 \\ Strategy 2 & 19.000 & 16.188 & 17.224 & 30.648 & 37.348 & 56.237 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Evaluation of different strategies in the entire wikinews-fr-100 dataset.  
* Hybrid modification \[S(t)=\frac{T_{\text{Rel}}\ast(T_{\text{Position}}+T_{\text{SenInfo}})}{T_{\text{ Case}}+\frac{T_{\text{SenInfo}}}{T_{\text{Rel}}}+\frac{T_{\text{SenInfo}}}{T_{\text{Rel}}}+ \frac{T_{\text{SenInfo}}}{T_{\text{Rel}}}}\] (14)  
The experimental results in Table 6 (with \(K\) = 5) show that adding the statistical feature \(T_{SenInfo}\) to the numerator can improve the evaluation results, suggesting that \(T_{SenInfo}\) has a direct relationship with the score. This implies that \(T_{SenInfo}\) reflects the average information of a word in a document, and when a word frequently appears in a key sentence with a large amount of information, its importance should be higher. Among the different heuristic weighting rules, (12) wins the best, indicating that \(T_{SenInfo}\) is highly important for the keyphrase extraction task. Furthermore, the statistical feature score obtained by multiplying \(T_{Rel}\) can significantly improve both the quality of keyphrases (P/R/F) and the ranking quality (MRR/MAP/Bpref).