Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: ESA: Entity Summarization with Attention   Entity summarization aims at creating brief but informative descriptions of
entities from knowledge graphs. While previous work mostly focused on
traditional techniques such as clustering algorithms and graph models, we ask
how to apply deep learning methods into this task. In this paper we propose
ESA, a neural network with supervised attention mechanisms for entity
summarization. Specifically, we calculate attention weights for facts in each
entity, and rank facts to generate reliable summaries. We explore techniques to
solve difficult learning problems presented by the ESA, and demonstrate the
effectiveness of our model in comparison with the state-of-the-art methods.
Experimental results show that our model improves the quality of the entity
summaries in both F-measure and MAP.

34
The categories of this paper are: chem-ph
22
The categories of this paper are: chao-dyn
35
The categories of this paper are: mtrl-th
33
The categories of this paper are: plasm-ph
3
The categories of this paper are: physics
2
The categories of this paper are: math
5
The categories of this paper are: gr-qc
4
The categories of this paper are: cond-mat
31
The categories of this paper are: ao-sci
30
The categories of this paper are: q-alg
32
The categories of this paper are: atom-ph
37
The categories of this paper are: supr-con
10
The categories of this paper are: q-bio
12
The categories of this paper are: nucl-th
8
The categories of this paper are: hep-ex
16
The categories of this paper are: stat