This paper addresses the problem of keyword extraction
from conversations, with the goal of using these keywords to
retrieve, for each short conversation fragment, a small number
of potentially relevant documents, which can be recommended to
participants. However, even a short fragment contains a variety
of words, which are potentially related to several topics; moreover,
using an automatic speech recognition (ASR) system introduces
errors among them. Therefore, it is difficult to infer precisely
the information needs of the conversation participants. We first
propose an algorithm to extract keywords from the output of an
ASR system (or a manual transcript for testing), which makes use
of topic modeling techniques and of a submodular reward function
which favors diversity in the keyword set, to match the potential
diversity of topics and reduce ASR noise. Then, we propose
a method to derive multiple topically separated queries from this
keyword set, in order to maximize the chances of making at least
one relevant recommendation when using these queries to search
over the English Wikipedia. The proposed methods are evaluated
in terms of relevance with respect to conversation fragments from
the Fisher, AMI, and ELEA conversational corpora, rated by several
human judges. The scores show that our proposal improves
over previous methods that consider only word frequency or topic
similarity, and represents a promising solution for a document recommender
system to be used in conversations.
Index Terms—Document recommendation, information retrieval,
keyword extraction, meeting analysis, topic modeling.