Leveraging Summarization for Unsupervised Dialogue Topic Segmentation (2024)

Abstract: Traditional approaches to dialogue segmentation perform reasonably well on synthetic or written dialogues but suffer when dealing with spoken, noisy dialogs. In addition, such methods require careful tuning of hyperparameters. We propose to leverage a novel approach that is based on dialogue summaries. Experiments on different datasets showed that the new approach outperforms popular state-of-the-art algorithms in unsupervised topic segmentation and requires less setup.

## introduction due to online communication's growth, topic segmentation is becoming increasingly relevant @xcite . the objective of topic segmentation is "to construct a system which identifies locations in a text stream where the topic changes" @xcite ). it is an example of a classic and still challenging task to automate @xcite . the challenging nature of this problem comes from several aspects. first, even for human annotators, topic segmentation might be a difficult task @xcite , which makes unsupervised approaches preferable. second, it is hard to handle unstructured textual datasets, especially for noisy spoken dialogues. driven by these challenges, we propose the use of summarization for unsupervised topic segmentation. summary is used to extract key information from less structured dialogue data and further enhanced by savitzky-golay smoothing (section 3.3) to handle high-frequency topic signals. using the chunking technique, we adopt this method for the limited context size of summarization models (section 3.3). it is experimentally demonstrated that the resulting approach holds * these authors contributed equally to this work good quality for different summary models, with context sizes varying from 5 hundred to 16 thousand tokens. u1: hello , i love fashion and hope to be a doctor one day . you ? u2: hello , i am an accountant from ohio . i have two boys and i am single . u3: student , male , divorced , ex named doug . i own a beetle . u4: oh a beetle how cool . i have a boring honda civic . what are your hobbies ? u5: wow ! i make book covers in my free time . i am published also . u6: how amazing ! i love to go hiking . what books have you written ? u7: angel investor and if i can help someone are my recent books . hiking huh ? u8: very cool ! yes , i love to hike you get some beautiful views ! u9: you are right ! if you take pictures you can sell them online . i love clothes . u10: i never thought of that . i love clothes as well . u11: i will attend au next year . i continue to write while studying medicine . u12: good luck , being a doctor is hard . maybe you will write medical books . u13: not a chance ! i love making up stories . medicine is too real sometimes . u14: ah , fiction books . as long as you are doing what you love you can not go wrong . s1: in his free time, he makes book covers s2: he is published s3: is single, divorced, he owns a beetle, a beetle and a honda civic s4: he will attend australia next year s5: he will continue to write while studying medicine to the best of our knowledge, there has been no other study focusing specifically on the summarybased unsupervised topic segmentation. for a study closest to our work, @xcite learned summarization and segmentation simultaneously to obtain robust sentence representations. we have made the source code publicly available foot_0 . ## conclusion and future work we have presented a novel summary-based approach for topic segmentation focusing on transcribed spoken dialogues. we leverage summarization for sentence vector extraction and combine it with the savitskiy-golay filtering @xcite to handle the noisy nature of transcribed spoken data. experiments on three real-world datasets demonstrate the effectiveness of the proposed model among the tested unsupervised approaches. we hope that our work can inspire further development of summary-based topic segmentation. more research steps are planned for summarization (including applying llms) and its use for text segmentation.