@article{3101,
  author = {Dongjun Wei, Yaxin Liu, Fuqing Zhu, Liangjun Zang, Wei Zhou, Jizhong Han, Songlin Hu},
  title = {Entity Summarization Using Supervised Mechanism},
  journal = {Journal of Electronic Systems},
  year = {2020},
  volume = {10},
  number = {3},
  doi = {https://doi.org/10.6025/jes/2020/10/3/95-101},
  url = {http://www.dline.info/jes/fulltext/v10n3/jesv10n3_2.pdf},
  abstract = {Extraction of data as entities from information descriptions is the main task in the entity analysis. Conventional techniques include clustering and graph whereas the current exercise tries to integrate deep learning in the model. The proposed model is a neural network model where we basically create a supervised mechanism. We measure and compute attention weights for facts. We then rank factual date for generate summaries. We work with methods to fix complex learning problems. While testing with extensive applications, the proposed technique increases the quality of entity summaries. This is arrived from the results of both F-measure and MAP compared with some state-of-the-art methods.
We have given the description with source codes at https://github.com/WeiDongjunGabriel/ESA1.},
}