Investigating Consistency in Query-Based Meeting Summarization: A
  Comparative Study of Different Embedding Methods
Chen Jia-Chen Guillem Senabre Allane Caron
With more and more advanced data analysis techniques emerging, people will
expect these techniques to be applied in more complex tasks and solve problems
in our daily lives. Text Summarization is one of famous applications in Natural
Language Processing (NLP) field. It aims to automatically generate summary with
important information based on a given context, which is important when you
have to deal with piles of documents. Summarization techniques can help capture
key points in a short time and bring convenience in works. One of applicable
situation is meeting summarization, especially for important meeting that tend
to be long, complicated, multi-topic and multi-person. Therefore, when people
want to review specific content from a meeting, it will be hard and
time-consuming to find the related spans in the meeting transcript. However,
most of previous works focus on doing summarization for newsletters, scientific
articles...etc, which have a clear document structure and an official format.
For the documents with complex structure like transcripts, we think those works
are not quite suitable for meeting summarization. Besides, the consistency of
summary is another issue common to be discussed in NLP field. To conquer
challenges of meeting summarization, we are inspired by "QMSum: A New Benchmark
for Query-based Multi-domain Meeting Summarization" proposed by Microsoft and
we also propose our Locater model designed to extract relevant spans based on
given transcript and query, which are then summarized by Summarizer model.
Furthermore, we perform a comparative study by applying different word
embedding techniques to improve summary consistency.