Joint Optimization of User-desired Content in Multi-document Summaries by Learning from User Feedback
Avinesh P. V. S.,Christian M. Meyer +1 more
- pp 1353-1363
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TLDR
This method interactively obtains user feedback to gradually improve the results of a state-of-the-art integer linear programming (ILP) framework for MDS and complement fully automatic methods in producing high-quality summaries with a minimum number of iterations and feedbacks.Abstract:
In this paper, we propose an extractive multi-document summarization (MDS) system using joint optimization and active learning for content selection grounded in user feedback. Our method interactively obtains user feedback to gradually improve the results of a state-of-the-art integer linear programming (ILP) framework for MDS. Our methods complement fully automatic methods in producing high-quality summaries with a minimum number of iterations and feedbacks. We conduct multiple simulation-based experiments and analyze the effect of feedback-based concept selection in the ILP setup in order to maximize the user-desired content in the summary.read more
Citations
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An Assessment of the Accuracy of Automatic Evaluation in Summarization | NIST
TL;DR: An assessment of the automatic evaluations used for multi-document summarization of news, and recommendations about how any evaluation, manual or automatic, should be used to find statistically significant differences between summarization systems.
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SUPERT: Towards New Frontiers in Unsupervised Evaluation Metrics for Multi-Document Summarization
Yang Gao,Wei Zhao,Steffen Eger +2 more
TL;DR: This work proposes SUPERT, which rates the quality of a summary by measuring its semantic similarity with a pseudo reference summary, i.e. selected salient sentences from the source documents, using contextualized embeddings and soft token alignment techniques.
Proceedings ArticleDOI
SUPERT: Towards New Frontiers in Unsupervised Evaluation Metrics for Multi-Document Summarization
Yang Gao,Wei Zhao,Steffen Eger +2 more
TL;DR: The authors propose to measure the quality of a summary by measuring its semantic similarity with a pseudo reference summary, i.e. selected salient sentences from the source documents, using contextualized embeddings and soft token alignment techniques.
Proceedings ArticleDOI
Adapting Neural Single-Document Summarization Model for Abstractive Multi-Document Summarization: A Pilot Study.
TL;DR: This paper proposes an approach to extend the neural abstractive model trained on large scale SDS data to the MDS task, which makes use of a small number of multi-document summaries for fine tuning.
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Towards a Neural Network Approach to Abstractive Multi-Document Summarization
TL;DR: This paper proposes an approach to extend the neural abstractive model trained on large scale SDS data to the MDS task, which makes use of a small number of multi-document summaries for fine tuning.
References
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Posted Content
Efficient Estimation of Word Representations in Vector Space
TL;DR: This paper proposed two novel model architectures for computing continuous vector representations of words from very large data sets, and the quality of these representations is measured in a word similarity task and the results are compared to the previously best performing techniques based on different types of neural networks.
Proceedings Article
ROUGE: A Package for Automatic Evaluation of Summaries
TL;DR: Four different RouGE measures are introduced: ROUGE-N, ROUge-L, R OUGE-W, and ROUAGE-S included in the Rouge summarization evaluation package and their evaluations.
Proceedings Article
Efficient Estimation of Word Representations in Vector Space
TL;DR: Two novel model architectures for computing continuous vector representations of words from very large data sets are proposed and it is shown that these vectors provide state-of-the-art performance on the authors' test set for measuring syntactic and semantic word similarities.
Proceedings Article
TextRank: Bringing Order into Text
Rada Mihalcea,Paul Tarau +1 more
TL;DR: TextRank, a graph-based ranking model for text processing, is introduced and it is shown how this model can be successfully used in natural language applications.