LeafNATS: An Open-Source Toolkit and Live Demo System for Neural Abstractive Text Summarization
By Tian Shi, Ping Wang, Chandan K. Reddy. 2019
15 citations recorded by the source corpus.
Neural abstractive text summarization (NATS) has received a lot of attention in the past few years from both industry and academia. In this paper, we introduce an open-source toolkit, namely LeafNATS, for training and evaluation of different sequence-to-sequence based models for the NATS task, and for deploying the pre-trained models to real-world applications. The toolkit is modularized and extensible in addition to maintaining competitive performance in the NATS task. A live news blogging system has also been implemented to demonstrate how these models can aid blog/news editors by providing them suggestions of headlines and summaries of their articles.
Subfields: Artificial Intelligence, Natural Language Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Mohit Bansal
- Zhe Gan
- Yejin Choi
- Noah A. Smith
- Jianfeng Gao
- Hannaneh Hajishirzi
- Jingjing Liu
- Yu Cheng
In Natural Language Processing
- Mohit Bansal
- Yejin Choi
- Hannaneh Hajishirzi
- Noah A. Smith
- Greg Durrett
- Luke S. Zettlemoyer
- Zhe Gan
- Jackie Chi Kit Cheung
Citations in the corpus
- PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization (2019, background): “Table 1: Results of PEGASUSLARGE and PEGASUSBASE on all downstream datasets compared with the previous SOTA, which are fetched from (Lewis et al., 2019; Shi et al., 2019; Fabbri et al., 2019; Koupaee and Wang, 2018; Kim et al., 2019; Subramanian et al., 2019; Song et al., 2019; Zhang and Tetreault, ”
- Factual Error Correction for Abstractive Summarization Models (2020, background): “Self-supervised methods have achieved success in a wide range of NLP tasks, and automatic summarization is no exception (Liu and Lapata, 2019; Lewis et al., 2019; Zhang et al., 2019a; Shi et al., 2019; Fabbri et al., 2019).”
- Multi-Fact Correction in Abstractive Text Summarization (2020, methodology): “Models such as BERT (Devlin et al., 2019), GPT (Radford et al., 2018) and BART (Lewis et al., 2020) have achieved new state-of-the-art performances on abstractive summarization (Liu and Lapata, 2019; Lewis et al., 2020; Zhang et al., 2020a; Shi et al., 2019; Fabbri et al., 2019).”
- Time Waits for No One! Analysis and Challenges of Temporal Misalignment (2021, methodology): “According to the leaderboard, the best three performing models are within a point of each other in Rouge-L (Shi et al., 2019, 2021; Mendes et al., 2019).”