As we move into the 21st century, with very rapid mobile communication and access to vast stores of information, we seem to be surrounded by more and more information, with less and less time or ability to digest it. The creation of the automatic summarization was really a genius human solution to solve this complicated problem. However, the application of this solution was too complex. In reality, there are many problems that need to be addressed before the promises of automatic text summarization can be fully realized. Basically, it is necessary to understand how humans summarize the text and then build the system based on that. Yet, individuals are so different in their thinking and interpretation that it is hard to create "gold-standard" summary against which output summaries will be evaluated. In this paper, we will discuss the basic concepts of this topic by giving the most relevant definitions, characterizations, types and the two different approaches of automatic text summarization: extraction and abstraction. Special attention is devoted to the extractive approach. It consists of selecting important sentences and paragraphs from the original text and concatenating them into shorter form. Broadly, the importance of sentences is decided based on statistical features of sentences. This approach avoids any efforts on deep text understanding. It is conceptually simple and easy to implement. KeywordsText summarization; Automatic text summarization; Abstractive approach; Extractive approach; Natural language processing.

As we move into the 21st century, with very rapid mobile communication and access to vast stores of information, we seem to be surrounded by more and more information, with less and less time or ability to digest it. The creation of the automatic summarization was really a genius human solution to solve this complicated problem. However, the application of this solution was too complex. In reality, there are many problems that need to be addressed before the promises of automatic text summarization can be fully realized. Basically, it is necessary to understand how humans summarize the text and then build the system based on that. Yet, individuals are so different in their thinking and interpretation that it is hard to create "gold-standard" summary against which output summaries will be evaluated. In this paper, we will discuss the basic concepts of this topic by giving the most relevant definitions, characterizations, types and the two different approaches of automatic text summarization: extraction and abstraction. Special attention is devoted to the extractive approach. It consists of selecting important sentences and paragraphs from the original text and concatenating them into shorter form. Broadly, the importance of sentences is decided based on statistical features of sentences. This approach avoids any efforts on deep text understanding. It is conceptually simple and easy to implement. KeywordsText summarization; Automatic text summarization; Abstractive approach; Extractive approach; Natural language processing.

As we move into the 21st century, with very rapid mobile communication and access to vast stores of information, we seem to be surrounded by more and more information, with less and less time or ability to digest it. The creation of the automatic summarization was really a genius human solution to solve this complicated problem. However, the application of this solution was too complex. In reality, there are many problems that need to be addressed before the promises of automatic text summarization can be fully realized. Basically, it is necessary to understand how humans summarize the text and then build the system based on that. Yet, individuals are so different in their thinking and interpretation that it is hard to create "gold-standard" summary against which output summaries will be evaluated. In this paper, we will discuss the basic concepts of this topic by giving the most relevant definitions, characterizations, types and the two different approaches of automatic text summarization: extraction and abstraction. Special attention is devoted to the extractive approach. It consists of selecting important sentences and paragraphs from the original text and concatenating them into shorter form. Broadly, the importance of sentences is decided based on statistical features of sentences. This approach avoids any efforts on deep text understanding. It is conceptually simple and easy to implement. KeywordsText summarization; Automatic text summarization; Abstractive approach; Extractive approach; Natural language processing.

As we move into the 21st century, with very rapid mobile communication and access to vast stores of information, we seem to be surrounded by more and more information, with less and less time or ability to digest it. The creation of the automatic summarization was really a genius human solution to solve this complicated problem. However, the application of this solution was too complex. In reality, there are many problems that need to be addressed before the promises of automatic text summarization can be fully realized. Basically, it is necessary to understand how humans summarize the text and then build the system based on that. Yet, individuals are so different in their thinking and interpretation that it is hard to create "gold-standard" summary against which output summaries will be evaluated. In this paper, we will discuss the basic concepts of this topic by giving the most relevant definitions, characterizations, types and the two different approaches of automatic text summarization: extraction and abstraction. Special attention is devoted to the extractive approach. It consists of selecting important sentences and paragraphs from the original text and concatenating them into shorter form. Broadly, the importance of sentences is decided based on statistical features of sentences. This approach avoids any efforts on deep text understanding. It is conceptually simple and easy to implement. KeywordsText summarization; Automatic text summarization; Abstractive approach; Extractive approach; Natural language processing.

The interest shown by some community of researchers to autonomous drones or UAVs (Unmanned Aerial Vehicles) has increased with the advent of wireless communication networks. These networks allow UAVs to cooperate more efficiently in an ad hoc manner in order to achieve specific tasks in specific environments. To do so, each drone navigates autonomously while staying connected with other nodes in its group via radio links. This connectivity can deliberately be maintained for a while constraining the mobility of the drones. This will be suitable for the drones involved in a given path of a given transmission between a source and a destination. This constraint could be removed at the end of the transmission process and the mobility of each concerned drone becomes again independent from the others. In this work, we have proposed a bio-inspired routing protocol for UAVs called BR- AODV. The protocol takes advantage of a well known ad hoc routing protocol for on-demand route computation, and the Boids of Reynolds mechanism for connectivity and route maintaining while data is being transmitted. The performances of BR-AODV were evaluated and compared to those of classical AODV routing protocol and the results show that BR-AODV outperforms AODV in terms of delay, throughput and packet loss.

The interest shown by some community of researchers to autonomous drones or UAVs (Unmanned Aerial Vehicles) has increased with the advent of wireless communication networks. These networks allow UAVs to cooperate more efficiently in an ad hoc manner in order to achieve specific tasks in specific environments. To do so, each drone navigates autonomously while staying connected with other nodes in its group via radio links. This connectivity can deliberately be maintained for a while constraining the mobility of the drones. This will be suitable for the drones involved in a given path of a given transmission between a source and a destination. This constraint could be removed at the end of the transmission process and the mobility of each concerned drone becomes again independent from the others. In this work, we have proposed a bio-inspired routing protocol for UAVs called BR- AODV. The protocol takes advantage of a well known ad hoc routing protocol for on-demand route computation, and the Boids of Reynolds mechanism for connectivity and route maintaining while data is being transmitted. The performances of BR-AODV were evaluated and compared to those of classical AODV routing protocol and the results show that BR-AODV outperforms AODV in terms of delay, throughput and packet loss.

The interest shown by some community of researchers to autonomous drones or UAVs (Unmanned Aerial Vehicles) has increased with the advent of wireless communication networks. These networks allow UAVs to cooperate more efficiently in an ad hoc manner in order to achieve specific tasks in specific environments. To do so, each drone navigates autonomously while staying connected with other nodes in its group via radio links. This connectivity can deliberately be maintained for a while constraining the mobility of the drones. This will be suitable for the drones involved in a given path of a given transmission between a source and a destination. This constraint could be removed at the end of the transmission process and the mobility of each concerned drone becomes again independent from the others. In this work, we have proposed a bio-inspired routing protocol for UAVs called BR- AODV. The protocol takes advantage of a well known ad hoc routing protocol for on-demand route computation, and the Boids of Reynolds mechanism for connectivity and route maintaining while data is being transmitted. The performances of BR-AODV were evaluated and compared to those of classical AODV routing protocol and the results show that BR-AODV outperforms AODV in terms of delay, throughput and packet loss.

The interest shown by some community of researchers to autonomous drones or UAVs (Unmanned Aerial Vehicles) has increased with the advent of wireless communication networks. These networks allow UAVs to cooperate more efficiently in an ad hoc manner in order to achieve specific tasks in specific environments. To do so, each drone navigates autonomously while staying connected with other nodes in its group via radio links. This connectivity can deliberately be maintained for a while constraining the mobility of the drones. This will be suitable for the drones involved in a given path of a given transmission between a source and a destination. This constraint could be removed at the end of the transmission process and the mobility of each concerned drone becomes again independent from the others. In this work, we have proposed a bio-inspired routing protocol for UAVs called BR- AODV. The protocol takes advantage of a well known ad hoc routing protocol for on-demand route computation, and the Boids of Reynolds mechanism for connectivity and route maintaining while data is being transmitted. The performances of BR-AODV were evaluated and compared to those of classical AODV routing protocol and the results show that BR-AODV outperforms AODV in terms of delay, throughput and packet loss.