QMSum: A New Benchmark for Query-based Multi-domain Meeting Summarization (2104.05938v1)

Abstract: Meetings are a key component of human collaboration. As increasing numbers of meetings are recorded and transcribed, meeting summaries have become essential to remind those who may or may not have attended the meetings about the key decisions made and the tasks to be completed. However, it is hard to create a single short summary that covers all the content of a long meeting involving multiple people and topics. In order to satisfy the needs of different types of users, we define a new query-based multi-domain meeting summarization task, where models have to select and summarize relevant spans of meetings in response to a query, and we introduce QMSum, a new benchmark for this task. QMSum consists of 1,808 query-summary pairs over 232 meetings in multiple domains. Besides, we investigate a locate-then-summarize method and evaluate a set of strong summarization baselines on the task. Experimental results and manual analysis reveal that QMSum presents significant challenges in long meeting summarization for future research. Dataset is available at \url{https://github.com/Yale-LILY/QMSum}.

A Critical Analysis of QMSum: A Benchmark for Query-based Multi-domain Meeting Summarization

The paper "QMSum: A New Benchmark for Query-based Multi-domain Meeting Summarization" addresses the growing demand for effective summarization systems capable of extracting and condensing relevant information from meeting transcripts according to specific queries. This research outlines the creation of QMSum, a novel dataset designed to refine and evaluate query-based summarization within multi-domain meeting contexts, thus advancing the capabilities of conversational understanding in NLP models.

Dataset and Task Design

QMSum is developed with a structured approach to meet the challenges of summarizing meetings, which are inherently long, multi-faceted, and involve multiple speakers. The dataset consists of 1,808 query-summary pairs derived from 232 meetings across diverse domains, including academic, product, and parliamentary committee meetings. This multi-domain setting provides a robust benchmark to evaluate generalization capabilities of summarization models across different meeting types.

The task defined by the authors revolves around query-based summarization, where models are tasked with generating succinct summaries from meeting transcripts guided by specific user queries. This involves extracting relevant portions of text and synthesizing them into coherent and informative summaries. To facilitate this task, the authors provide a hierarchical annotation structure within QMSum, encompassing main topics, queries, and summaries, alongside the associated relevant spans within the text.

Methodology

The authors propose a locate-then-summarize methodology to tackle the query-based summarization challenge. This approach divides the task into two stages:

1. Location Phase: Utilizing models like Pointer Networks and a hierarchical ranking-based model, the task is to locate relevant utterances within meeting transcripts per the given queries. The hierarchical ranking model notably achieves impressive ROUGE-L recall scores, indicating efficient extraction of pertinent text spans.
2. Summarization Phase: Leveraging state-of-the-art models such as BART and HMNet, which use the outputs of the Locator as inputs, the task aims to generate summaries responsive to the given queries. Performance assessments indicate that HMNet, in particular, shows outstanding results due to its hierarchical structure suited for processing lengthy documents.

Experimental Findings

Results demonstrate that current models face significant challenges in query-based summarization. Using ROUGE scores as evaluation metrics, models exhibit varying degrees of success across different query types. Notably, queries involving personal opinions, and reasons present higher difficulties in both model summarization and human evaluation.

The cross-domain experimentation reveals that models trained on a single domain struggle to generalize effectively to other domains, emphasizing the importance of multi-domain training provided by the QMSum dataset.

Implications and Future Directions

The introduction of QMSum has direct implications for the development of intelligent systems capable of handling complex meeting data. The dataset and proposed task framework can serve as foundational tools for refining algorithms in meeting summarization. The challenges identified, such as managing long transcripts and ensuring factual consistency, highlight areas for future research. Future work may involve integrating more sophisticated natural language understanding and generation techniques, potentially incorporating few-shot learning approaches to enhance adaptability.

Moreover, addressing the factual consistency and relevance of generated summaries is crucial. This emphasizes the need for advanced evaluation metrics that go beyond current norms to account for the multi-dimensionality of meeting transcripts and queries.

In conclusion, QMSum sets a significant challenge and benchmark for the field, pushing the boundaries on how effectively summarization models can parse and generate meaningful content from dialogue-heavy contexts such as meetings. The ongoing development and refinement of these models will have far-reaching effects on automated meeting assistance technologies and organizational efficiency in data processing.