Domain Adaptation for Summarizing Conversations

The goal of summarization in natural language processing is to create abridged and informative versions of documents. A popular approach is supervised extrac-tive summarization: given a training source corpus of documents with sentences labeled with their informativeness, train a model to select sentences from a target document and produce an extract. Conversational text is challenging to summarize because it is less formal, its structure depends on the modality or domain, and few annotated corpora exist. We use a labeled corpus of meeting transcripts as the source, and attempt to summarize a different target domain, threaded emails. We study two domain adap-tation scenarios: a supervised scenario in which some labeled target domain data is available for training, and an unsupervised scenario with only unlabeled data in the target and labeled data available in a related but different domain. We implement several recent domain adaptation algorithms and perform a comparative study of their performance. We also compare the effectiveness o