Title Generation with Quasi-Synchronous Grammar
By Kristian Woodsend, Yansong Feng, Mirella Lapata. 2010
47 citations recorded by the source corpus.
Subfields: 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 Natural Language Processing
- Graham Neubig
- Mohit Bansal
- Mirella Lapata
- Jun Suzuki
- Masaaki Nagata
- Bowen Zhou
- Pengfei Liu
- Caiming Xiong
Citations in the corpus
- Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond (2016, methodology): “, 2000), compression using weighted tree-transformation rules (Cohn and Lapata, 2008) and quasi-synchronous grammar approaches (Woodsend et al., 2010).”
- Controlling Output Length in Neural Encoder-Decoders (2016, methodology): “Traditional approaches to this task focus on word deletion using rule-based (Dorr et al., 2003; Zajic et al., 2004) or statistical (Woodsend et al., 2010; Galanis and Androutsopoulos, 2010; Filippova and Strube, 2008; Filippova and Altun, 2013; Filip-pova et al., 2015) methods.”
- Collective Generation of Natural Image Descriptions (2012, unknown): “Some recent research proposed very elegant approaches to summarization using ILP for collective content planning and/or surface realization (e.g., Martins and Smith (2009), Woodsend and Lapata (2010), Woodsend et al. (2010)).”
- Sequence-to-Sequence Learning with Latent Neural Grammars (2021, unknown): “Quasi-synchronous grammars have enjoyed applications across a wide range of domains including in machine translation [103, 42, 43], question answering [112], paraphrase detection [27], sentence simplification [117, 116], and parser projection [104].”
- SEQˆ3: Differentiable Sequence-to-Sequence-to-Sequence Autoencoder for Unsupervised Abstractive Sentence Compression (2019, unknown): “…(Zhou et al., 2017) 36.15 17.54 33.63 words-lvt5k-1sent (Nallapati et al., 2016) 36 Model R-1 R-2 R-L T OPIARY (Zajic et al., 2007) 25.12 6.46 20.12 Woodsend et al. (2010) 22 6 17 ABS (Rush et al., 2015) 28 components, unlike works that rely on reinforcement learning (Miao and Blunsom, 2016; Wang…”
- Abstract Text Summarization with a Convolutional Seq2seq Model (2019, methodology): “Reference [19] utilized quasi-synchronous grammar methods to generate summaries.”