We describe our multi-task learning based ap- proach for summarization of real-life dialogues as part of the DialogSum Challenge shared task at INLG 2022. Our approach intends to im- prove the main task of abstractive summariza- tion of dialogues through the auxiliary tasks of extractive summarization, novelty detection and language modeling. We conduct extensive experimentation with different combinations of tasks and compare the results. In addition, we also incorporate the topic information provided with the dataset to perform topic-aware sum- marization. We report the results of automatic evaluation of the generated summaries in terms of ROUGE and BERTScore.