2021 IEEE Spoken Language Technology Workshop (SLT) 2021

DOI: 10.1109/slt48900.2021.9383568

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Personalized Extractive Summarization for a News Dialogue System

Hirokatsu Takatsu

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,

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,

Yoichi Matsuyama

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et al.

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Cited by 3 publications

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“…We used EoIT β (efficiency of information transmission) (Takatsu et al, 2021) as the evaluation metric.…”

Section: Evaluation Metricsmentioning

confidence: 99%

Personalized Extractive Summarization Using an Ising Machine Towards Real-time Generation of Efficient and Coherent Dialogue Scenarios

et al. 2021

Proceedings of the 3rd Workshop on Natural Language Processing for Conversational AI

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We propose a personalized dialogue scenario generation system which transmits efficient and coherent information with a real-time extractive summarization method optimized by an Ising machine. The summarization problem is formulated as a quadratic unconstraint binary optimization (QUBO) problem, which extracts sentences that maximize the sum of the degree of user's interest in the sentences of documents with the discourse structure of each document and the total utterance time as constraints. To evaluate the proposed method, we constructed a news article corpus with annotations of the discourse structure, users' profiles, and interests in sentences and topics. The experimental results confirmed that a Digital Annealer, which is a simulated annealing-based Ising machine, can solve our QUBO model in a practical time without violating the constraints using this dataset.

“…We used EoIT β (efficiency of information transmission) (Takatsu et al, 2021) as the evaluation metric.…”

Section: Evaluation Metricsmentioning

confidence: 99%

Personalized Extractive Summarization Using an Ising Machine Towards Real-time Generation of Efficient and Coherent Dialogue Scenarios

et al. 2021

Proceedings of the 3rd Workshop on Natural Language Processing for Conversational AI

Self Cite

View full text Add to dashboard Cite

We propose a personalized dialogue scenario generation system which transmits efficient and coherent information with a real-time extractive summarization method optimized by an Ising machine. The summarization problem is formulated as a quadratic unconstraint binary optimization (QUBO) problem, which extracts sentences that maximize the sum of the degree of user's interest in the sentences of documents with the discourse structure of each document and the total utterance time as constraints. To evaluate the proposed method, we constructed a news article corpus with annotations of the discourse structure, users' profiles, and interests in sentences and topics. The experimental results confirmed that a Digital Annealer, which is a simulated annealing-based Ising machine, can solve our QUBO model in a practical time without violating the constraints using this dataset.

Personalized Extractive Summarization with Discourse Structure Constraints Towards Efficient and Coherent Dialog-Based News Delivery

Ando²,

et al. 2022

Lecture Notes in Electrical Engineering

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Customized Point Generation for Text Summarization

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et al. 2022

2022 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS)

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