Papers by Bruce Croft

2 papers
AREDSUM: Adaptive Redundancy-Aware Iterative Sentence Ranking for Extractive Document Summarization (2021.eacl-main)

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Challenge: Existing studies on redundancy are focused on salience alone.
Approach: They propose to combine salience and novelty to score redundancy in extractive summarization systems . they also propose to balance saliance and redundancies by scoring redundants first .
Outcome: Empirical results show that AREDSUM-CTX scores salience first, then learns to balance saliency and redundancy.
Transfer Learning for Context-Aware Question Matching in Information-seeking Conversations in E-commerce (P18-2)

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Challenge: Recent researches focus on deep learning and reinforcement learning for multi-turn information seeking conversation systems.
Approach: They propose an efficient and effective multi-turn conversation model based on convolutional neural networks and extend it to adapt the knowledge learned from a resource-rich domain to enhance the performance.
Outcome: The proposed model performs better than the existing model on an industrial chatbot called AliMe Assist.

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