In the recent few years, a lot of advancement has been made in Urdu linguistics. There are many portals and news websites that are generating a huge amount of data every day. However, there is still no publicly available dataset nor any framework available for automatic Urdu extractive ...
We propose a broad set of features that considers additional features in the fitness function. Keywords Evolutionary; Genetic; Features; Weights; Extractive; Summarization; Term Frequency ; References [1] A. Abuobieda, N. Salim, M.S. Binwahlan, A.H. Osman Differential evolution ...
An abstract is the most crucial element that may convince readers to read the complete text of a scientific publication. However, studies show that in terms of organization, readability, and style, abstracts are also among the most troublesome parts of the pertinent manuscript. The ultimate goal ...
but the number of sentences in the questions is large. In Step 2, we can use any classifier even with a large number of parameters thanks to the availability of large-scale data. In our experiments, we examine neural network and logistic regression classifiers. In ...
Terms Derived from Frequent Sequences for Extractive Text Summarization - Ledeneva, Gelbukh - 2008 () Citation Context ...or example, they can be words; deciding what objects will count as terms is the task of term selection. Different extractive summarization methods can be characterized by how...
The main idea of this paper is to rank Maximal Frequent Sequences (MFS) in order to identify the most important information in a text. MFS are considered as nodes of a graph in term selection step, and then are ranked in term weighting step using a graph-based algorithm. We show that ...
In general, our framework consists of three parts: a shared document encoder, a hierarchical attention mechanism-based decoder and an extractor. As Fig. 1 shows, we encode the document in a hierarchical way [Fig. 1(1), (2)] in order to address the long-term dependency problem. Then, ...
LexRank[33] is another graph-based algorithm that relies onPageRank.Its key difference is that each sentence is represented as a vector of theTF-IDF(term frequency—inverse document frequency) scores of the words it contains, while the relationship between these sentence vectors is measured using...
Both systems use the Vector Space Model (VSM) in the summarization phase, where the weighting scheme is based on VSM and uses two measures, term frequency and inverse document frequency. In AQBTSS, each sentence is compared to the user query to find relevant sentences, whereas in ACBTSS, ...
The main idea of this paper is to rank Maximal Frequent Sequences (MFS) in order to identify the most important information in a text. MFS are considered as nodes of a graph in term selection step, and then are ranked in term weighting step using a graph-based algorithm. We show that ...