Neural Models for Key Phrase Extraction and Question Generation

By Sandeep Subramanian, Tong Wang, Xingdi Yuan, Saizheng Zhang, Adam Trischler, Yoshua Bengio. 2018

64 citations recorded by the source corpus.

We propose a two-stage neural model to tackle question generation from documents. First, our model estimates the probability that word sequences in a document are ones that a human would pick when selecting candidate answers by training a neural key-phrase extractor on the answers in a question-answering corpus. Predicted key phrases then act as target answers and condition a sequence-to-sequence question-generation model with a copy mechanism. Empirically, our key-phrase extraction model significantly outperforms an entity-tagging baseline and existing rule-based approaches. We further demonstrate that our question generation system formulates fluent, answerable questions from key phrases. This two-stage system could be used to augment or generate reading comprehension datasets, which may be leveraged to improve machine reading systems or in educational settings.

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

In Natural Language Processing

Citations in the corpus

Addressing Semantic Drift in Question Generation for Semi-Supervised Question Answering (2019, methodology): “Some previous works have proposed methods to detect key phrases from a paragraph and automatically extract potential answer spans (Yang et al., 2017; Du and Cardie, 2018; Subramanian et al., 2018).”
Learning to Generate Questions by Learning to Recover Answer-containing Sentences (2021, background): “Subramanian et al. (2018) show that neural models generate better candidate answers from a given paragraph than using off-the-shelf tools or selecting named entities and noun phrases.”
Mixture Content Selection for Diverse Sequence Generation (2019, methodology): “From our initial experiments, we found this parallel focus inference to be more effective than auto-regressive pointing mechanism (Vinyals et al., 2015; Subramanian et al., 2018).”
QURIOUS: Question Generation Pretraining for Text Generation (2020, methodology): “For answer-focused question generation, earlier work (Duan et al., 2017; Subramanian et al., 2018; Nema et al., 2019) has mostly focused on the factoid-based question answering dataset such as SQuAD (Rajpurkar et al., 2016).”