Summarizing Dialogues with Negative Cues (2022)

Abstract: Abstractive dialogue summarization aims to convert a long dialogue content into its short form where the salient information is preserved while the redundant pieces are ignored. Different from the well-structured text, such as news and scientific articles, dialogues often consist of utterances coming from two or more interlocutors, where the conversations are often informal, verbose, and repetitive, sprinkled with false-starts, backchanneling, reconfirmations, hesitations, speaker interruptions and the salient information is often scattered across the whole chat. The above properties of conversations make it difficult to directly concentrate on scattered outstanding utterances and thus present new challenges of summarizing dialogues. In this work, rather than directly forcing a summarization system to merely pay more attention to the salient pieces, we propose to explicitly have the model perceive the redundant parts of an input dialogue history during the training phase. To be specific, we design two strategies to construct examples without salient pieces as negative cues. Then, the sequence-to-sequence likelihood loss is cooperated with the unlikelihood objective to drive the model to focus less on the unimportant information and also pay more attention to the salient pieces. Extensive experiments on the benchmark dataset demonstrate that our simple method significantly outperforms the baselines with regard to both semantic matching and factual consistent based metrics. The human evaluation also proves the performance gains.

## introduction online conversations have become an indispensable manner of communication in our daily work and life, where people tend to exchange their ideas, share information, consult via textual messages. especially in the era of information explosion, it is figure 1 : an example of dialogue with its summary. green: nouns, italic: salient utterances. much more challenging and time-consuming to go through all the conversation content and catch key ideas @xcite . thus, it is paramount to present the most salient facts, instead of the whole lengthy dialogue history, which is beneficial to various scenarios and applications, such as online customer service @xcite , meeting and email thread summary @xcite . therefore, this work focuses on the abstractive dialogue summarization task, aiming to automatically convert the long dialogue history into its shorter form retaining the most essential and informative content yet getting rid of the dispensable pieces, exemplified by a dialogue-summary instance in figure 1 . one intuitive solution to summarizing dialogue content is to directly adopt existing summarization systems @xcite @xcite designed for wellstructured text, such as news and scientific articles @xcite or to employ hierarchical models to capture features from different turns of different speakers @xcite . unfortunately, succinctly summarizing dialogue content presents new challenges due to intrinsic properties of conversations. unlike the field of well-organized text merely from a single person, dialogues often consist of utterances coming from two or more interlocutors, where the conversations are often informal, verbose and repetitive, sprinkled with false-starts, backchannels, reconfirmations, hesitations, speaker interruptions @xcite and the key information is often scattered throughout the whole chat. the above properties of conversations make it difficult to concentrate on the scattered salient utterances. recent studies incorporate intrinsic information of dialogues to handle the challenges for summarizing dialogues, such as topic features @xcite @xcite , dialogue acts (goo and chen, 2018), conversation stages (chen and yang, 2020) and coreference information @xcite . the main idea of such existing summarization systems is to directly learn the salient information of the input dialogues with various architecture designed or extra knowledge added. differently, this work proposes to train a dialogue summarization system by explicitly telling the model the unimportant/redundant pieces of an input dialogue, so that the model is able to focus less on the given negative hints and pay more attention to the salient information. to be specific, we design two strategies to construct negative examples, namely noun drop, and salient utterance drop. then, we design an unlikelihood objective to model the probability of producing the gold summary given a negative example. the model is then trained based on the summation of likelihood and unlikelihood objectives. extensive experiments on the samsum dataset demonstrate that our proposed method outperforms baselines on both semantic matching and factual consistent based metrics. the human evaluation also proves the performance improvements of our simple yet effective method. ## conclusion recent studies involved dialogue studies (e.g., topical information, coreference information, and dia-logue acts) to make the model directly pay more attention to salient parts. however, the characteristics of dialogue content make it challenging to concentrate on scattered outstanding utterances. rather, in this work, we propose a simple yet effective approach to explicitly tell a model the redundant pieces of a dialogue and thus focus more on the salient ones. we proposed two strategies to construct negative samples with redundant information and designed an unlikelihood objective to force the model learn less from redundant information, in other words, learning more from the salient pieces. experiments on the benchmark dataset demonstrate the efficacy of the proposed model. in the future, we plan to investigate other strategies for constructing negative examples and replace the unlikelihood objective with the ranking loss.