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.

通过引入指导训练到对话中，我们提出了一种三步方法来合成高质量的基于查询的摘要三元组。通过在三个摘要数据集上训练统一模型 InstructDS，我们扩展了对话摘要模型的能力，并在人类主观评估中得到了更高的泛化能力和忠实度。

使用查询聚合的指导性对话摘要

Instructive Dialogue Summarization with Query Aggregations

In this paper, we present a model for generating summaries of text documents
with respect to a query. This is known as query-based summarization. We adapt
an existing dataset of news article summaries for the task and train a
pointer-generator model using this dataset. The generated summaries are
evaluated by measuring similarity to reference summaries. Our results show that
a neural network summarization model, similar to existing neural network models
for abstractive summarization, can be constructed to make use of queries to
produce targeted summaries.

本文提出了一种基于查询的文章摘要生成模型，使用新闻文章摘要数据集训练了指针生成模型，通过与参考摘要的相似度评估生成的摘要，结果表明可以构建类似于抽象式摘要的神经网络模型，使用查询生成有针对性的摘要。

基于神经网络的基于查询的抽象摘要生成

Query-Based Abstractive Summarization Using Neural Networks

Abstractive summarization aims to generate a shorter version of the document
covering all the salient points in a compact and coherent fashion. On the other
hand, query-based summarization highlights those points that are relevant in
the context of a given query. The encode-attend-decode paradigm has achieved
notable success in machine translation, extractive summarization, dialog
systems, etc. But it suffers from the drawback of generation of repeated
phrases. In this work we propose a model for the query-based summarization task
based on the encode-attend-decode paradigm with two key additions (i) a query
attention model (in addition to document attention model) which learns to focus
on different portions of the query at different time steps (instead of using a
static representation for the query) and (ii) a new diversity based attention
model which aims to alleviate the problem of repeating phrases in the summary.
In order to enable the testing of this model we introduce a new query-based
summarization dataset building on debatepedia. Our experiments show that with
these two additions the proposed model clearly outperforms vanilla
encode-attend-decode models with a gain of 28% (absolute) in ROUGE-L scores.

该研究提出了一种基于编码 - 注意 - 解码范式的查询式摘要模型，具有查询注意模型和多样性基于注意模型。实验表明，该模型相对于基础模型具有 28% 的 ROUGE-L 分数的增益。