Dialogue summarization aims to create a concise and coherent overview of a conversation between two or more people. Recent advances in language models have significantly improved this process, but accurately summarizing dialogues is still challenging due to the need to understand the interactions between speakers to capture the most relevant information. This study focuses on goal-oriented human-human dialogues, incorporating task-related information into the summarization process to produce summaries that are more semantically accurate. It explores multitask approaches that combine summarization with language comprehension tasks and introduces new methods for summary selection and evaluation based on semantic analysis. The study tests these methods on the DECODA corpus, a collection of French spoken dialogues from a call center, showing that integrating models and task-related information improves the accuracy of summaries, even with varying levels of word error rates.