Neural Abstractive Summarization Algorithm
A Neural Abstractive Summarization Algorithm is an abstractive summarization algorithm that is a Neural NLP algorithm.
References
2018
- (Liu et al., 2018) ⇒ Peter J. Liu, Mohammad Saleh, Etienne Pot, Ben Goodrich, Ryan Sepassi, Lukasz Kaiser, and Noam Shazeer. (2018). “Generating Wikipedia by Summarizing Long Sequences.” In: Proceedings of the Sixth International Conference on Learning Representations (ICLR-2018).
- QUOTE: Neural abstractive summarization was pioneered in Rush et al. (2015), where they train headline generation models using the English Gigaword corpus (Graff & Cieri, 2003), consisting of news articles from number of publishers. However, the task is more akin to sentence paraphrasing than summarization as only the first sentence of an article is used to predict the headline, another sentence. RNN-based encoder-decoder models with attention (seq2seq) perform very well on this task in both ROUGE (Lin, 2004), an automatic metric often used in summarization, and human evaluation (Chopra et al., 2016).
In Nallapati et al. (2016), an abstractive summarization dataset is proposed by modifying a question-answering dataset of news articles paired with story highlights from Daily Mail and CNN. This task is more difficult than headline-generation because the information used in the highlights may come from many parts of the article and not only the first sentence. One downside of the dataset is that it has an order-of-magnitude fewer parallel examples (310k vs. 3.8M) to learn from. Standard seq2seq models with attention do less well, and a number of techniques are used to augment performance. Another downside is that it is unclear what the guidelines are for creating story highlights and it is obvious that there are significant stylistic differences between the two news publishers. In our work we also train neural abstractive models, but in the multi-document regime with Wikipedia.
- QUOTE: Neural abstractive summarization was pioneered in Rush et al. (2015), where they train headline generation models using the English Gigaword corpus (Graff & Cieri, 2003), consisting of news articles from number of publishers. However, the task is more akin to sentence paraphrasing than summarization as only the first sentence of an article is used to predict the headline, another sentence. RNN-based encoder-decoder models with attention (seq2seq) perform very well on this task in both ROUGE (Lin, 2004), an automatic metric often used in summarization, and human evaluation (Chopra et al., 2016).
2018b
- (Paulus et al., 2017) ⇒ Romain Paulus, Caiming Xiong, and Richard Socher. (2017). “A Deep Reinforced Model for Abstractive Summarization.” arXiv preprint arXiv:1705.04304
2015
- (Rush et al., 2015) ⇒ Alexander M. Rush, Sumit Chopra, and Jason Weston. (2015). “A Neural Attention Model for Abstractive Sentence Summarization.” In: Proceedings of EMNLP-2015.
- QUOTE: Summarization based on text extraction is inherently limited, but generation-style abstractive methods have proven challenging to build. In this work, we propose a fully data-driven approach to abstractive sentence summarization. ...