  Conventional dialogue summarization methods directly generate summaries and
do not consider user's specific interests. This poses challenges in cases where
the users are more focused on particular topics or aspects. With the
advancement of instruction-finetuned language models, we introduce
instruction-tuning to dialogues to expand the capability set of dialogue
summarization models. To overcome the scarcity of instructive dialogue
summarization data, we propose a three-step approach to synthesize high-quality
query-based summarization triples. This process involves summary-anchored query
generation, query filtering, and query-based summary generation. By training a
unified model called InstructDS (Instructive Dialogue Summarization) on three
summarization datasets with multi-purpose instructive triples, we expand the
capability of dialogue summarization models. We evaluate our method on four
datasets, including dialogue summarization and dialogue reading comprehension.
Experimental results show that our approach outperforms the state-of-the-art
models and even models with larger sizes. Additionally, our model exhibits
higher generalizability and faithfulness, as confirmed by human subjective
evaluations.
