More Like this

1. NetReAct: Interactive Learning for Network Summarization

   Amiri, Sorour; Adhikari, Bijaya; Wenskovitch, John; Rodriguez, Alexander; Dowling, Michelle; North, Chris; Prakash, B. Aditya (December 2020, NeurIPS 2020 Human and Model in the Loop Evaluation and Training Strategies (HAMLETS) Workshop)

   null (Ed.)
2. Cosine similarity for multiplex network summarization

   https://doi.org/10.1145/3487351.3488331  

   Polychronopoulou, Athanasia; Zhou, Fang; Obradovic, Zoran (January 2021, ASONAM '21: Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining)
3. Multiplex Graph Neural Network for Extractive Text Summarization

   https://doi.org/10.18653/v1/2021.emnlp-main.11  

   Jing, Baoyu; You, Zeyu; Yang, Tao; Fan, Wei; Tong, Hanghang (January 2021, EMNLP)

   Extractive text summarization aims at extract- ing the most representative sentences from a given document as its summary. To extract a good summary from a long text document, sen- tence embedding plays an important role. Re- cent studies have leveraged graph neural net- works to capture the inter-sentential relation- ship (e.g., the discourse graph) to learn con- textual sentence embedding. However, those approaches neither consider multiple types of inter-sentential relationships (e.g., semantic similarity & natural connection), nor model intra-sentential relationships (e.g, semantic & syntactic relationship among words). To ad- dress these problems, we propose a novel Mul- tiplex Graph Convolutional Network (Multi- GCN) to jointly model different types of rela- tionships among sentences and words. Based on Multi-GCN, we propose a Multiplex Graph Summarization (Multi-GraS) model for extrac- tive text summarization. Finally, we evaluate the proposed models on the CNN/DailyMail benchmark dataset to demonstrate the effec- tiveness of our method. 

   more » « less
4. Summarization Programs: Interpretable Abstractive Summarization with Neural Modular Trees.

   Saha, S.; Zhang, S.; Hase, P.; Bansal, M. (May 2023, Proceedings of the Eleventh International Conference on Learning Representations)
5. Summarization Programs: Interpretable Abstractive Summarization with Neural Modular Trees

   Swarnadeep Saha, Shiyue Zhang (January 2023, The International Conference on Learning Representations (ICLR))