Neural Extractive Summarization with Side Information
By Nikos Papasarantopoulos, Mirella Lapata, Shay B. Cohen. 2017
77 citations recorded by the source corpus.
Most extractive summarization methods focus on the main body of the document from which sentences need to be extracted. However, the gist of the document may lie in side information, such as the title and image captions which are often available for newswire articles. We propose to explore side information in the context of single-document extractive summarization. We develop a framework for single-document summarization composed of a hierarchical document encoder and an attention-based extractor with attention over side information. We evaluate our model on a large scale news dataset. We show that extractive summarization with side information consistently outperforms its counterpart that does not use any side information, in terms of both informativeness and fluency.
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
- Jianfeng Gao
- Mohit Bansal
- Richard Socher
- Zhe Gan
- Luke S. Zettlemoyer
- Lawrence Carin
- Graham Neubig
In Natural Language Processing
- Caiming Xiong
- Richard Socher
- Mohit Bansal
- Bryan McCann
- Jianfeng Gao
- Mirella Lapata
- Graham Neubig
- Zhe Gan
Citations in the corpus
- Don’t Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization (2018, background): “…has consistently attracted attention (Cheng and Lapata, 2016; Durrett et al., 2016; Nallapati et al., 2016, 2017; See et al., 2017; Tan and Wan, 2017; Narayan et al., 2017; Fan et al., 2017; Paulus et al., 2018; Pasunuru and Bansal, 2018; Celikyilmaz et al., 2018; Narayan et al., 2018a,b).”
- Neural Text Summarization: A Critical Evaluation (2019, background): “In the most common case, models are trained as word-or sentence-level classifiers that predict whether a fragment should be included in the summary (Nallapati et al., 2016b, 2017; Narayan et al., 2017; Liu et al., 2019; Xu and Durrett, 2019).”
- Ranking Sentences for Extractive Summarization with Reinforcement Learning (2018, methodology): “,sn), we follow common practice and feed sentences in reverse order (Sutskever et al., 2014; Li et al., 2015; Filippova et al., 2015; Narayan et al., 2017).”
- Deconvolutional Paragraph Representation Learning (2017, background): “These representations are typically a required first step toward more applied tasks, such as sentiment analysis [1, 2, 3, 4], machine translation [5, 6, 7], dialogue systems [8, 9, 10] and text summarization [11, 12, 13].”