Topic-aware Pointer-Generator Networks for Summarizing Spoken Conversations
This work addresses conversation summarization, a domain with limited resources, by adapting neural models to better capture dialogue structure, though it is incremental as it builds on existing pointer-generator methods.
The paper tackled the problem of summarizing spoken conversations by proposing a topic-aware pointer-generator network to handle scattered topic information and linguistic characteristics like topic diffusion, achieving significant performance improvements over baselines with more efficient learning and robust results.
Due to the lack of publicly available resources, conversation summarization has received far less attention than text summarization. As the purpose of conversations is to exchange information between at least two interlocutors, key information about a certain topic is often scattered and spanned across multiple utterances and turns from different speakers. This phenomenon is more pronounced during spoken conversations, where speech characteristics such as backchanneling and false-starts might interrupt the topical flow. Moreover, topic diffusion and (intra-utterance) topic drift are also more common in human-to-human conversations. Such linguistic characteristics of dialogue topics make sentence-level extractive summarization approaches used in spoken documents ill-suited for summarizing conversations. Pointer-generator networks have effectively demonstrated its strength at integrating extractive and abstractive capabilities through neural modeling in text summarization. To the best of our knowledge, to date no one has adopted it for summarizing conversations. In this work, we propose a topic-aware architecture to exploit the inherent hierarchical structure in conversations to further adapt the pointer-generator model. Our approach significantly outperforms competitive baselines, achieves more efficient learning outcomes, and attains more robust performance.