mlsanityData to 7 Oct 2026

Summary Grounded Conversation Generation

arXiv 2106.03337 ↗First posted 7 Jun 2021cs.CLcited by 5

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Abstract (arXiv)

Many conversation datasets have been constructed in the recent years using crowdsourcing. However, the data collection process can be time consuming and presents many challenges to ensure data quality. Since language generation has improved immensely in recent years with the advancement of pre-trained language models, we investigate how such models can be utilized to generate entire conversations, given only a summary of a conversation as the input. We explore three approaches to generate summary grounded conversations, and evaluate the generated conversations using automatic measures and human judgements. We also show that the accuracy of conversation summarization can be improved by augmenting a conversation summarization dataset with generated conversations.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 21 of its 29 references that are in the map. First posted on arXiv 7 Jun 2021.

Builds on

21 of 29 references.

References · 21

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9 more
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Built on by

Cited by year

Citing papers per year: 2021: 1; 2022: 2; 2023: 0; 2024: 1; 2025: 1.

5 papers.

  1. 2021A Survey on Dialogue Summarization: Recent Advances and New Frontierscited by 51
  2. 2024Multi-Document Grounded Multi-Turn Synthetic Dialog Generationcited by 7
  3. 2022Meeting Summarization: A Survey of the State of the Artcited by 3
  4. 2025QUARTZ : QA-based Unsupervised Abstractive Refinement for Task-oriented Dialogue Summarizationcited by 2
  5. 2022Heuristic-based Inter-training to Improve Few-shot Multi-perspective Dialog Summarizationcited by 1