We present a novel approach to automatically extracting summary excerpts from audio and video. Our approach is to maximize the average similarity between the excerpt and the source. We first calculate a similarity matrix by comparing each pair of time samples using a quantitative similarity measure. To determine the segment with highest average similarity, we maximize the summation of the self-...

This paper evaluates the performance of different similarity measures in the context of document summarization. For this purpose in this paper a simple and effective sentence extractive technique is used. The proposed method is based on evaluation of relevance score of sentence. Many measures are available for the calculation of inter sentence relationships. To calculate a similarity between se...

In this paper, we describe a new vector similarity measure associated with a convex cost function. Given two vectors, we determine the surface normals of the convex function at the vectors. The angle between the two surface normals is the similarity measure. Convex cost function can be the negative entropy function, total variation (TV) function and filtered variation function. The convex cost ...

This paper describes an emotion-based approach to acquire sentiment similarity of word pairs with respect to their senses. Sentiment similarity indicates the similarity between two words from their underlying sentiments. Our approach is built on a model which maps from senses of words to vectors of twelve basic emotions. The emotional vectors are used to measure the sentiment similarity of word...

Clustering is one of the very powerful and widely used technique in information retrieval. All clustering methods works on finding relationship among data objects. There are various similarity measures used along with criterion functions to find similarity between documents like cosine, jaccard etc. Clustering efficiency and performance is highly dependent on the accuracy of the similarity meas...

In the fuzzy set theory, information  measures play a paramount role in several areas such as decision making, pattern recognition etc. In this paper, similarity measure based on cosine function and entropy measures based on logarithmic function for IFSs are proposed. Comparisons of proposed similarity and entropy measures with the existing ones are listed. Numerical results limpidly betoken th...

This paper presents a novel approach to the semi-supervised learning of Information Extraction patterns. The method makes use of more complex patterns than previous approaches and determines their similarity using a measure inspired by recent work using kernel methods (Culotta and Sorensen, 2004). Experiments show that the proposed similarity measure outperforms a previously reported measure ba...