258 research outputs found
Generating Abstractive Summaries from Meeting Transcripts
Summaries of meetings are very important as they convey the essential content
of discussions in a concise form. 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.Comment: 10 pages, Proceedings of the 2015 ACM Symposium on Document
Engineering, DocEng' 201
Action-Item-Driven Summarization of Long Meeting Transcripts
The increased prevalence of online meetings has significantly enhanced the
practicality of a model that can automatically generate the summary of a given
meeting. This paper introduces a novel and effective approach to automate the
generation of meeting summaries. Current approaches to this problem generate
general and basic summaries, considering the meeting simply as a long dialogue.
However, our novel algorithms can generate abstractive meeting summaries that
are driven by the action items contained in the meeting transcript. This is
done by recursively generating summaries and employing our action-item
extraction algorithm for each section of the meeting in parallel. All of these
sectional summaries are then combined and summarized together to create a
coherent and action-item-driven summary. In addition, this paper introduces
three novel methods for dividing up long transcripts into topic-based sections
to improve the time efficiency of our algorithm, as well as to resolve the
issue of large language models (LLMs) forgetting long-term dependencies. Our
pipeline achieved a BERTScore of 64.98 across the AMI corpus, which is an
approximately 4.98% increase from the current state-of-the-art result produced
by a fine-tuned BART (Bidirectional and Auto-Regressive Transformers) model.Comment: Accepted into the 7th International Conference on Natural Language
Processing and Information Retrieval (NLPIR 2023
Joint Modeling of Content and Discourse Relations in Dialogues
We present a joint modeling approach to identify salient discussion points in
spoken meetings as well as to label the discourse relations between speaker
turns. A variation of our model is also discussed when discourse relations are
treated as latent variables. Experimental results on two popular meeting
corpora show that our joint model can outperform state-of-the-art approaches
for both phrase-based content selection and discourse relation prediction
tasks. We also evaluate our model on predicting the consistency among team
members' understanding of their group decisions. Classifiers trained with
features constructed from our model achieve significant better predictive
performance than the state-of-the-art.Comment: Accepted by ACL 2017. 11 page
Improving Query-Focused Meeting Summarization with Query-Relevant Knowledge
Query-Focused Meeting Summarization (QFMS) aims to generate a summary of a
given meeting transcript conditioned upon a query. The main challenges for QFMS
are the long input text length and sparse query-relevant information in the
meeting transcript. In this paper, we propose a knowledge-enhanced two-stage
framework called Knowledge-Aware Summarizer (KAS) to tackle the challenges. In
the first stage, we introduce knowledge-aware scores to improve the
query-relevant segment extraction. In the second stage, we incorporate
query-relevant knowledge in the summary generation. Experimental results on the
QMSum dataset show that our approach achieves state-of-the-art performance.
Further analysis proves the competency of our methods in generating relevant
and faithful summaries.Comment: AACL 2023 Finding
MeetingBank: A Benchmark Dataset for Meeting Summarization
As the number of recorded meetings increases, it becomes increasingly
important to utilize summarization technology to create useful summaries of
these recordings. However, there is a crucial lack of annotated meeting corpora
for developing this technology, as it can be hard to collect meetings,
especially when the topics discussed are confidential. Furthermore, meeting
summaries written by experienced writers are scarce, making it hard for
abstractive summarizers to produce sensible output without a reliable
reference. This lack of annotated corpora has hindered the development of
meeting summarization technology. In this paper, we present MeetingBank, a new
benchmark dataset of city council meetings over the past decade. MeetingBank is
unique among other meeting corpora due to its divide-and-conquer approach,
which involves dividing professionally written meeting minutes into shorter
passages and aligning them with specific segments of the meeting. This breaks
down the process of summarizing a lengthy meeting into smaller, more manageable
tasks. The dataset provides a new testbed of various meeting summarization
systems and also allows the public to gain insight into how council decisions
are made. We make the collection, including meeting video links, transcripts,
reference summaries, agenda, and other metadata, publicly available to
facilitate the development of better meeting summarization techniques. Our
dataset can be accessed at: https://meetingbank.github.ioComment: ACL 2023 Long Pape
PREME: Preference-based Meeting Exploration through an Interactive Questionnaire
The recent increase in the volume of online meetings necessitates automated
tools for managing and organizing the material, especially when an attendee has
missed the discussion and needs assistance in quickly exploring it. In this
work, we propose a novel end-to-end framework for generating interactive
questionnaires for preference-based meeting exploration. As a result, users are
supplied with a list of suggested questions reflecting their preferences. Since
the task is new, we introduce an automatic evaluation strategy. Namely, it
measures how much the generated questions via questionnaire are answerable to
ensure factual correctness and covers the source meeting for the depth of
possible exploration
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