Beyond the imitation game: Quantifying and extrapolating the capabilities of language models
Language models demonstrate both quantitative improvement and new qualitative
capabilities with increasing scale. Despite their potentially transformative impact, these new …
capabilities with increasing scale. Despite their potentially transformative impact, these new …
Multi-document summarization via deep learning techniques: A survey
Multi-document summarization (MDS) is an effective tool for information aggregation that
generates an informative and concise summary from a cluster of topic-related documents …
generates an informative and concise summary from a cluster of topic-related documents …
Automatic text summarization methods: A comprehensive review
Text summarization is the process of condensing a long text into a shorter version by
maintaining the key information and its meaning. Automatic text summarization can save …
maintaining the key information and its meaning. Automatic text summarization can save …
Summarunner: A recurrent neural network based sequence model for extractive summarization of documents
Abstract We present SummaRuNNer, a Recurrent Neural Network (RNN) based sequence
model for extractive summarization of documents and show that it achieves performance …
model for extractive summarization of documents and show that it achieves performance …
Ranking sentences for extractive summarization with reinforcement learning
Single document summarization is the task of producing a shorter version of a document
while preserving its principal information content. In this paper we conceptualize extractive …
while preserving its principal information content. In this paper we conceptualize extractive …
Abstractive document summarization with a graph-based attentional neural model
Abstractive summarization is the ultimate goal of document summarization research, but
previously it is less investigated due to the immaturity of text generation techniques …
previously it is less investigated due to the immaturity of text generation techniques …
Pre-training methods in information retrieval
The core of information retrieval (IR) is to identify relevant information from large-scale
resources and return it as a ranked list to respond to user's information need. In recent years …
resources and return it as a ranked list to respond to user's information need. In recent years …
A unified model for extractive and abstractive summarization using inconsistency loss
WT Hsu, CK Lin, MY Lee, K Min, J Tang… - arxiv preprint arxiv …, 2018 - arxiv.org
We propose a unified model combining the strength of extractive and abstractive
summarization. On the one hand, a simple extractive model can obtain sentence-level …
summarization. On the one hand, a simple extractive model can obtain sentence-level …
Sentence centrality revisited for unsupervised summarization
Single document summarization has enjoyed renewed interests in recent years thanks to the
popularity of neural network models and the availability of large-scale datasets. In this paper …
popularity of neural network models and the availability of large-scale datasets. In this paper …
Graph-based neural multi-document summarization
We propose a neural multi-document summarization (MDS) system that incorporates
sentence relation graphs. We employ a Graph Convolutional Network (GCN) on the relation …
sentence relation graphs. We employ a Graph Convolutional Network (GCN) on the relation …