  Recently abstractive spoken language summarization raises emerging research
interest, and neural sequence-to-sequence approaches have brought significant
performance improvement. However, summarizing long meeting transcripts remains
challenging. Due to the large length of source contents and targeted summaries,
neural models are prone to be distracted on the context, and produce summaries
with degraded quality. Moreover, pre-trained language models with input length
limitations cannot be readily applied to long sequences. In this work, we first
analyze the linguistic characteristics of meeting transcripts on a
representative corpus, and find that the sentences comprising the summary
correlate with the meeting agenda. Based on this observation, we propose a
dynamic sliding window strategy for meeting summarization. Experimental results
show that performance benefit from the proposed method, and outputs obtain
higher factual consistency than the base model.
