From spoken dialogue to formal summary: An utterance rewriting for dialogue summarization (2022)

Abstract: Due to the dialogue characteristics of unstructured contexts and multi-parties with first-person perspective, many successful text summarization works have failed when dealing with dialogue summarization. In dialogue summarization task, the input dialogue is usually spoken style with ellipsis and co-references but the output summaries are more formal and complete. Therefore, the dialogue summarization model should be able to complete the ellipsis content and co-reference information and then produce a suitable summary accordingly. However, the current state-of-the-art models pay more attention on the topic or structure of summary, rather than the consistency of dialogue summary with its input dialogue context, which may suffer from the personal and logical inconsistency problem. In this paper, we propose a new model, named ReWriteSum, to tackle this problem. Firstly, an utterance rewriter is conducted to complete the ellipsis content of dialogue content and then obtain the rewriting utterances. Then, the co-reference data augmentation mechanism is utilized to replace the referential person name with its specific name to enhance the personal information. Finally, the rewriting utterances and the co-reference replacement data are used in the standard BART model. Experimental results on both SAMSum and DialSum datasets show that our ReWriteSum significantly outperforms baseline models, in terms of both metric-based and human evaluations. Further analysis on multi-speakers also shows that ReWriteSum can obtain relatively higher improvement with more speakers, validating the correctness and property of ReWriteSum.

## introduction despite many existing text summarization works on single-speaker written documents, such as news and encyclopedia articles @xcite for this task, it is clear that there is a big gap between the input spoken dialogue and the output formal summaries. that is, in dialogue, users tend to use many incomplete utterances, which al-ways omit or refer back to entities appeared in the history, called ellipsis and co-reference. but the summary is usually formal and written, which contains rich and complete salient information. here we give two examples, as shown in however, the current models pay more attention on introducing intrinsic information, such as dialogue acts @xcite , key point sequence @xcite and co-reference information @xcite . they demonstrate that the introduction of intrinsic information and human annotation is effective in improving the quality of summary generation. however, dialogue acts and key point sequence require a lot of human effort, so they can not be widely used in applications. the coreference chain is integrated by gnn, which only pays attention to the referencing information of entities but not supplement and restore the referred and omitted pronouns in the dialogue utterances, resulting in the misunderstanding of omitted contents. more importantly, they all ignore the consistency between the dialogue summary and its source dialogue, which may lead to the personal and logical inconsistency problem caused by multi-speakers. in this paper, we propose a new model, namely rewritesum, to tackle this problem. the core idea is to use the utterance rewriting mechanism to complete the omitted content and utilize the data augmentation strategy to enhance the co-reference information. specifically, we first use the utter-ance rewriter to complete the ellipsis content in dialogue contexts, and then obtain the rewritten utterances dataset. then, we use the co-reference data augmentation mechanism to replace the referential person name with its specific name with a certain probability to enhance the personal information. finally, we use both the rewritten utterances and the co-reference replacement data as input, and utilize the state-of-the-art model bart to generate the corresponding summary. in our experiments, we use two public datasets to evaluate our proposed models, i.e. samsum and dialsum. the results show that rewritesum has the ability to produce more consistent and suitable summary than traditional summarization models. besides, we conduct an analysis on multispeakers, and the results show that the rewritesum obtains relatively higher improvement with more speakers, which indicates that the incomplete utterance rewriting and co-reference data augmentation mechanism by our model are reasonable. ## model in this section, we will describe our rewritesum model in detail, with architecture shown in figure 1 . rewritesum consists of an incomplete utterance rewriter, a co-reference data augmentation and a transformer-based bart summarization model. for incomplete utterance rewriting, we establish a word-level edit matrix @xcite , whose element determines three editing operations: substitute, insert and none. to obtain the edit matrix, we conduct three neural networks, as shown in figure 2: a bilstm-based context layer to obtain the word representation, an encoder layer to model the local information, and a segmentation layer to model the global information. for co-reference data augmentation, we replace the co-reference word with its specific name entity through a co-reference resolution model @xcite and then augment the dataset with these replacement data. specifically, we firstly use the co-reference resolution model to obtain the co-reference chain of the entire dialogue, then replace the pronoun in the co-reference chain with the specific name entity based on a certain probability. finally, we utilize these replacement data to augment the personal information for dialogue summarization task. ## experiments in this section, we conduct experiments on two english dialogue summarization datasets sam-sum @xcite and dialsum @xcite to evaluate our proposed method. ## conclusion in this work, we propose a new dialogue summarization model, namely rewritesum, which leverages incomplete utterance rewriting and coreference data augmentation mechanism to generate summaries for dialogue. our motivation comes from the fact that there are a lot of ellipsis and demonstrative pronouns in the dialogue, which seriously affects the quality of dialogue summary generation. our core idea is to utilize the incomplete utterance rewriting module to complete the ellipsis information in the dialogue content and enhance the personal entities with the co-reference data augmentation mechanism. we conduct exper-iments on both samsum and dialsum datasets, and the results on both quantitative and qualitative analysis verify the effectiveness of our proposed model. therefore, we obtain the conclusion that the incomplete utterance rewriting and co-reference data augmentation are effective for improving the quality of generation for dialogue summarization.