# Solving Multi-Document Summarization as an Orienteering Problem
## 7 Conclusions  
In this paper, we proposed to solve the extractive MDS problem by encoding it as an OP, and approximate its solution by an ACS meta-heuristic. The implemented system MDS-OP was evaluated on two benchmark corpora including DUC 2004 (Task 2) and MultiLing 2015 (MMS task) using several ROUGE metrics and the three official evaluation methods adopted at MultiLing 2015 (AutoSummENG, MeMoG, and NPowER). Its performance was compared to those of 26 systems, which participated in DUC 2004 and MultiLing 2015 competitions. The F-measure scores show that MDS-OP outperformed the 16 systems that participated at DUC 2004 (Task 2) in terms of ROUGE-1 and ROUGE-L. It also outperformed the 10 systems which participated at MultiLing 2015 (MMS task) in terms of ROUGE-SU4, AutoSummENG, MeMoG, and NPowER. The performance of MDS-OP in terms of other ROUGE metrics (ROUGE-2, ROUGE-3, ROUGE-4, and ROUGE-W) ranked it among the best three systems. These results demonstrate the effectiveness of the proposed approach for MDS.  
MDS-OP does not need a training phase as required by machine-learning based systems. It relies on only statistical and graph-based features. However, the robustness of its performance depends on the tuning of the parameters of ACS.  
In a future work, we plan to study other semantic features and their impact on the performance of MDS-OP, as well as other SI metaheuristics which were examined for solving OP. In addition, to improve the readability of a summary, the text coherency can be included as a second objective, which would motivate a bi-objective formulation and solving of the MDS problem. This would be performed by  
Figure 14: NPowER scores on MultiLing 2015 (MMS task).  
Figure 13: MeMoG scores on MultiLing 2015 (MMS task).  
adding the coherence scores between pairs of sentences in the OP graph and optimizing the order of summary sentences. Different methods would be examined to calculate the local coherence scores between sentences pairs.  
Conceptualization, A.A.-S. and M.E.B.M.; Methodology, A.A.-S. and M.E.B.M.; Software, A.A.-S.; Validation, A.A.-S. and M.E.B.M.; Formal Analysis, A.A.-S.; Investigation, A.A.-S. and M.E.B.M.; Writing--Original Draft Preparation, A.A.-S.; Writing--Review & Editing, M.E.B.M.; Resources, A.A.-S., available benchmark data, and open source tools; Supervision, M.E.B.M.  
This research received no external funding.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} ACO & Ant colony optimization \\ ACS & Ant colony system \\ ABC & Artificial bee colony \\ AS & Ant system \\ AutoSummENG & AUTomatic SUMMARY Evaluation based on N-gram Graphs \\ CS & Cuckoo search \\ DUC & Document understanding conference \\ GA & Genetic Algorithm \\ hLDA & Hierarchical Latent Dirichlet Allocation \\ HMM & Hidden Markov model \\ LCS & Longest common subsequence \\ MDS & Multi-document summarization \\ MeMoG & Merged Model Graph \\ MMS & Multilingual multi-document summarization \\ NP & Noun phrase \\ NPowER & N-gram graph Powered Evaluation via Regression \\ OP & Orienteering problem \\ PSO & Particle swarm optimization \\ ROUGE & Recall-Oriented Understudy for Gisting Evaluation \\ SI & Swarm intelligence \\ TAC & Text analysis conference \\ TSP & Traveling salesman problem \\ TF-IDF & Term frequency times inverse document frequency \\ TF-ISF & Term frequency times inverse sentence frequency \\ \end{tabular}