Summaries of meetings are very important as they convey the essential content of discussions in a concise form. Both participants and non-participants are interested in the summaries of meetings to plan for their future work. Generally, it is time consuming to read and understand the whole documents. Therefore, summaries play an important role as the readers are interested in only the important context of discussions. In this work, we address the task of meeting document summarization. Automatic summarization systems on meeting conversations developed so far have been primarily extractive, resulting in unacceptable summaries that are hard to read. The extracted utterances contain disfluencies that affect the quality of the extractive summaries. To make summaries much more readable, we propose an approach to generating abstractive summaries by fusing important content from several utterances. We first separate meeting transcripts into various topic segments, and then identify the important utterances in each segment using a supervised learning approach. The important utterances are then combined together to generate a one-sentence summary. In the text generation step, the dependency parses of the utterances in each segment are combined together to create a directed graph. The most informative and well-formed sub-graph obtained by integer linear programming (ILP) is selected to generate a one-sentence summary for each topic segment. The ILP formulation reduces disfluencies by leveraging grammatical relations that are more prominent in non-conversational style of text, and therefore generates summaries that is comparable to human-written abstractive summaries. Experimental results show that our method can generate more informative summaries than the baselines. In addition, readability assessments by human judges as well as log-likelihood estimates obtained from the dependency parser show that our generated summaries are significantly readable and well-formed.
@cite_1: In this paper we present a complete system for automatically generating natural language abstracts of meeting conversations. This system is comprised of components relating to interpretation of the meeting documents according to a meeting ontology, transformation or content selection from that source representation to a summary representation, and generation of new summary text. In a formative user study, we compare this approach to gold-standard human abstracts and extracts to gauge the usefulness of the different summary types for browsing meeting conversations. We find that our automatically generated summaries are ranked significantly higher than human-selected extracts on coherence and usability criteria. More generally, users demonstrate a strong preference for abstract-style summaries over extracts.
Liu and Liu used sentence compression to generate summaries of meetings. However, they reported that the quality of the generated summaries are not so good and there is a potential limit to apply such methods to summarization. Murray mapped conversations to an ontology that was complemented with a Natural language generation (NLG) component used for transforming utterances to summaries. The corresponding full summarization system was later presented in @cite_1 , where a user study was conducted on the abstractive summaries that were generated. However, the full summarization system involved extensive manual labor to set specific speakers, entities, etc. in a template before using an NLG realizer to generate the summaries. Lu and Cardie proposed a method that learns templates from the human written summaries and generates the summaries of decisions and actions of meetings by using the best set of templates for a particular summary ranked using a greedy approach. In contrast, we cannot use templates because we assume that the type of a conversation (action, decision, etc) is not known apriori.