Ziqiang Cao

Incorporating Relevant Knowledge in Context Modeling and Response Generation

2018 · arXiv (Cornell University)

To sustain engaging conversation, it is critical for chatbots to make good use of relevant knowledge. Equipped with a knowledge base, chatbots are able to extract conversation-related attributes and entities to facilitate context modeling and …

Contrastive Learning with High-Quality and Low-Quality Augmented Data for Query-Focused Summarization

2024

Unlike general text summarization, Query-focused summarization (QFS) is severely limited by insufficient datasets, forcing previous research to transform datasets from other tasks into QFS format for data augmentation. However, this approach has resulted in two …

DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset

2017 · arXiv (Cornell University)

We develop a high-quality multi-turn dialog dataset, DailyDialog, which is intriguing in several aspects. The language is human-written and less noisy. The dialogues in the dataset reflect our daily communication way and cover various topics …

Faithful to the Original: Fact Aware Neural Abstractive Summarization

2018 · Proceedings of the AAAI Conference on Artificial Intelligence

Unlike extractive summarization, abstractive summarization has to fuse different parts of the source text, which inclines to create fake facts. Our preliminary study reveals nearly 30% of the outputs from a state-of-the-art neural summarization system …

Retrieve, Rerank and Rewrite: Soft Template Based Neural Summarization

2018

Most previous seq2seq summarization systems purely depend on the source text to generate summaries, which tends to work unstably. Inspired by the traditional template-based summarization approaches, this paper proposes to use existing summaries as soft …

Faithful to the Original: Fact Aware Neural Abstractive Summarization

2017 · arXiv (Cornell University)

Unlike extractive summarization, abstractive summarization has to fuse different parts of the source text, which inclines to create fake facts. Our preliminary study reveals nearly 30% of the outputs from a state-of-the-art neural summarization system …

Ranking with Recursive Neural Networks and Its Application to Multi-Document Summarization

2015 · Proceedings of the AAAI Conference on Artificial Intelligence

We develop a Ranking framework upon Recursive Neural Networks (R2N2) to rank sentences for multi-document summarization. It formulates the sentence ranking task as a hierarchical regression process, which simultaneously measures the salience of a sentence …