Detecting Information-Dense Texts in Multiple News Domains
By Yinfei Yang, Ani Nenkova. 2014
17 citations recorded by the source corpus.
We introduce the task of identifying information-dense texts,which report important factual information in direct, succinct manner. We describe a procedure that allows us to label automatically a large training corpus of New York Times texts.We train a classifier based on lexical, discourse and unlexicalized syntactic features and test its performance on a set of manually annotated articles from business, U.S. international relations, sports and science domains. Our results indicate that the task is feasible and that both syntactic and lexicalfeatures are highly predictive for the distinction. We observe considerable variation of prediction accuracy across domains and find that domain-specific models are more accurate.
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
- Caiming Xiong
- Richard Socher
- Mohit Bansal
- Yejin Choi
- Graham Neubig
- Noah A. Smith
- Luke S. Zettlemoyer
- Bryan McCann
In Natural Language Processing
- Caiming Xiong
- Richard Socher
- Mohit Bansal
- Yejin Choi
- Bryan McCann
- Graham Neubig
- Nitish Shirish Keskar
- Yoav Goldberg
Citations in the corpus
- A Deep Reinforced Model for Abstractive Summarization (2017, methodology): “…systems (Hong and Nenkova, 2014; Li et al., 2016) or closely-related models for predicting the importance of a phrase in an article (Yang and Nenkova, 2014; Nye and Nenkova, 2015; Hong et al., 2015), we are the first group to run an end-to-end abstrac-tive summarization model on the…”
- Controlling Linguistic Style Aspects in Neural Language Generation (2017, background): “Computational modeling of stylistic properties has been the focus of several lines of study, i.e. (Pavlick and Tetreault, 2016; Yang and Nenkova, 2014; Pavlick and Nenkova, 2015).”
- Neural Text Summarization: A Critical Evaluation (2019, methodology): “It has been primarily used for extractive summarization (Hong and Nenkova, 2014; Li et al., 2016) and phrase-importance prediction (Yang and Nenkova, 2014; Nye and Nenkova, 2015).”
- System Combination for Multi-document Summarization (2015, methodology): “We also include features that compute the information density of the first sentence that each word appears in (Yang and Nenkova, 2014).”