# Improved Text Summarization of News Articles Using GA-HC and PSO-HC
## 3 Propose Model
In this research work, we used non-dominated sorting algorithm-II (NSGA-II) to optimize the rank of sentences. NSGA-II preserved the best individuals of each population and used to create offspring. NSGA-II is fast converging algorithm as

Figure 3: Flow diagram of proposed model. compared to conventional GA. Algorithm 2 represents the working of GA. Moreover, the crossover process is illustrated in Figure 4.

### Particle Swarm Optimization (PSO)

PSO is inspired from the social behavior of bird of flocks [48]. PSO is population based, which is based on the concept of swarm intelligence and is capable of solving NP-hard problems. PSO used personal best and global best experience to update the position of swarms. In PSO, particle represents the candidate solution and particle best position is considered as the optimized solution. The position of the particles depends on velocity of particles and calculated as follows

\[v_{k}\ =wv_{k}+c_{1}r_{1}(pBest_{k}-d_{k})+c_{2}r_{2}(gBest_{k}-d_{k})\]

Figure 2.0

where \(v_{k}\,\ d_{k}\) represent the velocity and position of \(kth\) particle, respectively. \(w,c_{1},r_{1},c_{2},r_{2}\) are adaptive parameters which are tuned to obtained best possible solution. The working of PSO is given in Algorithm 3. Figure 2.1 GA crossover and mutation procedure. ### Complete Ranking Model

First the features are extracted from the sentences in the clusters. The best solution obtained from GA is multiplied with all the feature scores, and, finally, all the features score are added and accumulate result is the rank of that sentence. The proposed PSO-HC and GA-HC is presented in Algorithms 4 and 5. ```
1. Begin
2. Let d be the input text document containing S sentences
3. Generate list of Big Vectors
4. Clusters are generated using agglomerative clustering algorithm
5. The clusters are passed to PSO ranking model
6. Final summary is obtained by combining top n selected sentences
7. End
```
**Algorithm 4**Proposed Model(PSO-HC)
```
1. Begin
2. Let d be the input text document containing S sentences
3. Generate list of Big Vectors
4. Clusters are generated using agglomerative clustering algorithm
5. Generate list of Big Vectors
6. Clusters are generated using agglomerative clustering algorithm
7. The clusters are passed to GA ranking mode
8. Final summary is obtained by combining top \(n\) selected sentences
9. End
```

**Algorithm 5**Proposed Model (GA-HC)

## 4 Experimental Setup and Results

### Dataset

For evaluating our proposed model, we used Document Understanding Conference (DUC) 2007 dataset and CNN/Daily mail dataset 2015. The dataset contains 10 main topics, and each topic contains four to five sub topics. Each sub topic contains 25 documents. The documents are news articles taken from various news sources. There are four summaries created by experts and two base summaries are considered. In addition, 30 summaries are submitted by the participants. CNN/Daily Mail is a text summary dataset. Human-made abstractive summary bullets were generated as questions (with one of the elements obscured) and stories as the appropriate passages from which the system is anticipated to answer the fill-in-the-blank question from news stories on CNN and Daily Mail websites. The scripts that crawl, extract, and produce pairs of excerpts and questions from these websites were released by the authors. The CNN articles were written between April 2007 and April 2015. The Daily Mail articles were written between June 2010 and April 2015. In all, the corpus has 286,817 training pairs, 13,368 validation pairs and 11,487 test pairs, as defined by their scripts. The source documents in the training set have 766 words spanning 29.74 sentences on an average while the summaries consist of 53 words and 3.72 sentences. ### Pre-Processing

Preprocessing is a process of cleaning the data and shaping the data for the model. Noise is present in the news data that effects the extraction of information, therefore preprocessing is used for cleaning the data from noise.