Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Conceptual Text Summarizer: A new model in continuous vector space   Traditional methods of summarization are not cost-effective and possible
today. Extractive summarization is a process that helps to extract the most
important sentences from a text automatically and generates a short informative
summary. In this work, we propose an unsupervised method to summarize Persian
texts. This method is a novel hybrid approach that clusters the concepts of the
text using deep learning and traditional statistical methods. First we produce
a word embedding based on Hamshahri2 corpus and a dictionary of word
frequencies. Then the proposed algorithm extracts the keywords of the document,
clusters its concepts, and finally ranks the sentences to produce the summary.
We evaluated the proposed method on Pasokh single-document corpus using the
ROUGE evaluation measure. Without using any hand-crafted features, our proposed
method achieves state-of-the-art results. We compared our unsupervised method
with the best supervised Persian methods and we achieved an overall improvement
of ROUGE-2 recall score of 7.5%.

28
The categories of this paper are: alg-geom
27
The categories of this paper are: comp-gas
17
The categories of this paper are: q-fin
29
The categories of this paper are: funct-an
20
The categories of this paper are: acc-phys
34
The categories of this paper are: chem-ph
11
The categories of this paper are: hep-lat
32
The categories of this paper are: atom-ph
3
The categories of this paper are: physics
13
The categories of this paper are: quant-ph
8
The categories of this paper are: hep-ex
10
The categories of this paper are: q-bio
16
The categories of this paper are: stat
9
The categories of this paper are: nlin
37
The categories of this paper are: supr-con
18
The categories of this paper are: econ