In this paper, we propose two methods to improve the Ad-Hoc On-Demand Distance-Vector (AODV) protocol. The main goal in the design of the protocol was to reduce the routing overhead, buffer overflow, end-to-end delay and increase the performance. A multi-path routing protocol is proposed which is based on AODV and Ant Colony Optimization (ACO). This protocol is refereed to Multi-Route AODV Ant routing (MRAA). Also we propose a load balancing method that uses all discovered paths simultaneously for transmitting data. In this method, data packets are balanced over discovered paths and energy consumption is distributed across many nodes through network. This protocol is refereed to Load Balanced Multi-Route AODV Ant routing algorithm (LBMRAA).

In this paper, we propose two methods to improve the Ad-Hoc On-Demand Distance-Vector (AODV) protocol. The main goal in the design of the protocol was to reduce the routing overhead, buffer overflow, end-to-end delay and increase the performance. A multi-path routing protocol is proposed which is based on AODV and Ant Colony Optimization (ACO). This protocol is refereed to Multi-Route AODV Ant routing (MRAA). Also we propose a load balancing method that uses all discovered paths simultaneously for transmitting data. In this method, data packets are balanced over discovered paths and energy consumption is distributed across many nodes through network. This protocol is refereed to Load Balanced Multi-Route AODV Ant routing algorithm (LBMRAA).

Working with the DUC 2002 collection for multi-document summarization, we considered two types of document sets: sets consisting of closely correlated documents with highly overlapped content; and sets of diverse documents covering a wide scope of topics. Intuitively, this suggests that creating a quality summary would be more difficult for the latter case. The two types of document sets can be identified automatically by our document graph approach. However, human evaluators were shown to be fairly insensitive to this difference. This was identified when they were asked to rank the performance of automated summarizers. In this paper, we examine and analyze our experiment in order to better understand this phenomenon and how we might address it to improve summarization.

Abstract - Text summarization is an important problem, which has numerous applications. This problem has been extensively studied and many approaches have been proposed in the literature for its solution. In this paper, we investigate a new approach that employs meta-search. In particular, summaries from several summarizers are evaluated and a new summary is formed from these summaries. Clearly, this meta-summarizer will produce a better summary than any of the others since it exploits the special features of all the sources. We have employed data from Document Understanding Conference 2002 (DUC-2002) and 5 different summarizers in our experiments. We have also created our own summarizer.

Summarization evaluation has been always a challenge to researchers in the document summarization field. Usually, human involvement is necessary to evaluate the quality of a summary. Here we present a new method for automatic evaluation of text summaries by using document graphs. Data from Document Understanding Conference 2002 (DUC- 2002) has been used in the experiment. We propose measuring the similarity between two summaries or between a summary and a document based on the concepts/entities and relations between them in the text.

Text summarization is an important problem since it has numerous applications. This problem has been extensively studied and many approaches have been pro-posed in the literature for its solution. One such interesting approach is that of posing summarization as an optimization problem and using genetic algorithms to solve this optimization problem. In this paper we present elegant randomized algorithms for summarization based on sampling. Our experimental results show that our algorithms yield nearly the same accuracy as the genetic algorithms while significantly saving on time. We have employed data from Document Understanding Conference 2002 and 2004 (DUC-2002, DUC-2004) in our experiments

Text summarization is an important problem,which has numerous applications. This problem has been extensively studied and many approaches have been proposed in the literature for its solution. One of the most challenging problems in the field of text summarization is generating a user-focused summary based on a query. In this paper, we investigate a new approach that tackles this problem and propose a new solution using document graphs. This is our first time to participate in Document Understanding Conferences.

Text summarization is an important problem, which has numerous applications. This problem has been extensively studied and many approaches have been proposed in the literature for its solution. One of the most challenging problems in the field of text summarization is generating a user-focused summary based on a query. In this paper, we investigate a new approach that tackles this problem and proposes a new solution using document graphs. We have employed data from Document Understanding Conference 2006 (DUC-2006).