Learning to Rank Utterances for Query-Focused Meeting Summarization (2023)

Abstract: Query-focused meeting summarization(QFMS) aims to generate a specific summary for the given query according to the meeting transcripts. Due to the conflict between long meetings and limited input size, previous works mainly adopt extract-then-summarize methods, which use extractors to simulate binary labels or ROUGE scores to extract utterances related to the query and then generate a summary. However, the previous approach fails to fully use the comparison between utterances. To the extractor, comparison orders are more important than specific scores. In this paper, we propose a Ranker-Generator framework. It learns to rank the utterances by comparing them in pairs and learning from the global orders, then uses top utterances as the generator’s input. We show that learning to rank utterances helps to select utterances related to the query effectively, and the summarizer can benefit from it. Experimental results on QMSum show that the proposed model outperforms all existing multi-stage models with fewer parameters.

## introduction query-focused meeting summarization(qfms) aims to summarize the crucial information for the given query into a concise passage according to the meeting transcripts. by responding to the query, qfms can meet the user's need to focus on a specific aspect or topic of the meeting @xcite . unlike the generic summary, qfms requires the summary depending on both the given query and meeting transcripts. previous works consist of end-to-end and twostage frameworks. the end-to-end models take the whole long meeting as the input. although some works such as hmnet @xcite and hatbart @xcite use hierarchical attention mechanism to alleviate the rapid growth * yajing xu is the corresponding author. in computational complexity, it's still faced with difficulties in training efficiency. the two-stage models extract utterances related to the query and then pass the concatenation of them to the generator. for qfms, the key information related to the query scatters in certain parts of the meeting. therefore, the two-stage framework is considered as a practical approach to balance experimental performance and computational efficiency in the long-input problems. the two-stage framework mainly includes the locator-generator and the simulator-generator approaches. as shown in figure 1 , in the first stage, the locator-generator @xcite framework considers it as a binary classification task. it predicts a binary label of whether the utterance is relevant to the query and uses cross-entropy loss to update parameters. but the hard binary labels can not reflect the relative quality. especially when the training data is limited by scarcity, the binary classification will have a large margin between positive and negative samples. so the simulator-generator @xcite it as a rouge score regression task. it simulates the rouge score and uses mse loss to update parameters. however, there is a gap between the extractor's ultimate objective and the objective of minimizing the absolute error between predicted scores and rouge scores. in fact, rather than specific scores, we care more about the relative orders of utterances. to make full use of the comparison information between samples, we propose a ranker-generator framework in this paper. to balance experimental effectiveness and computational efficiency, the framework contains three steps. first, the utterances would be divided into samples. we conduct pairwise ranking to get an order for each sample. second, the top utterances in different samples would be fed into the re-ranker, which would conduct listwise ranking to get a global order. finally, the top k utterances would be concatenated and passed to the generator. to summarize, our contributions are as follows: (1) this paper demonstrates that, by enhancing the accuracy of extracting query-relevant utterances, the generator can make the summary more related to the query. (2) we propose a ranker-generator framework to extract query-relevant utterances by learning to rank discourse to improve the quality of the generated summaries. (3) experimental results show that the proposed model outperforms existing multi-stage models with fewer model parameters. ## method the architecture of our method is illustrated in figure 2. our model consists of a two-stage ranking step and a generating step. the utterances would be ranked by the sample pairwise ranking module and the global listwise re-ranking module, and top of them can be passed to the generator to produce the final summary. ## conclusion this paper proposes a new multi-stage framework for qfms. it learns to rank the meeting utterances by pairwise and listwise comparison between them. by selecting the utterances with high query-relevance scores as the generator's input, the generator can produce high-quality summaries that are more relevant to the query. the experiments demonstrate the effectiveness of the ranker-generator framework.