Structure-Aware Abstractive Conversation Summarization via Discourse and Action Graphs (2021)

Abstract: Abstractive conversation summarization has received much attention recently. However, these generated summaries often suffer from insufficient, redundant, or incorrect content, largely due to the unstructured and complex characteristics of human-human interactions. To this end, we propose to explicitly model the rich structures in conversations for more precise and accurate conversation summarization, by first incorporating discourse relations between utterances and action triples (“who-doing-what”) in utterances through structured graphs to better encode conversations, and then designing a multi-granularity decoder to generate summaries by combining all levels of information. Experiments show that our proposed models outperform state-of-the-art methods and generalize well in other domains in terms of both automatic evaluations and human judgments. We have publicly released our code at https://github.com/GT-SALT/Structure-Aware-BART .

## introduction online interaction has become an indispensable component of everyday life and people are increasingly using textual conversations to exchange ideas, make plans, and share information. however, it is time-consuming to recap and grasp all the core content within every complex conversation @xcite . as a result, how to organize massive everyday interactions into natural, concise, and informative text, i.e., abstractive conversation summarization, starts to gain importance. significant progress has been made on abstractive summarization for structured document via pointer generator @xcite , reinforcement methods @xcite and pre-trained models @xcite @xcite . despite the huge success, it is challenging to directly apply document models to summarize conversations, due to @xcite . the annotated summary is simon was on the phone before, so he didn't here helen calling. simon will fetch helen some tissues. a set of inherent differences between conversations and documents @xcite . first, speaker interruptions like repetitions, false-starts, and hesitations are frequent in conversations @xcite , and key information resides in different portions of a conversation. these unstructured properties pose challenges for models to focus on salient contents that are necessary for generating both abstractive and informative summaries. second, there is more than one speaker in conversations and people interact with each other in different language styles @xcite . the complex interactions among multiple speakers make it harder for mod-els to identify and associate speakers with correct actions so as to generate factual summaries. in order to summarize the unstructured and complex conversations, a growing body of research has been conducted, such as transferring document summarization methods to conversation settings @xcite , adopting hierarchical models @xcite , or incorporating conversation structures like topic segmentation @xcite @xcite , dialogue acts (goo and @xcite , and conversation stages @xcite . however, current approaches still face challenges in terms of succinctness and faithfulness, as most prior studies (i) fail to explicitly model dependencies between utterances which can help identify salient portions of conversations @xcite , and (ii) lack structured representations @xcite to learn the associations between speakers, actions and events. we argue that these rich linguistic structures associated with conversations are key components towards generating abstractive and factual conversation summaries. to this end, we present a structure-aware sequence-to-sequence model, in which we equip abstractive conversation summarization models with rich conversation structures through two types of graphs: discourse relation graph and action graph. discourse relation graphs are constructed based on dependency-based discourse relations @xcite @xcite between intertwined utterances, where each elementary discourse unit (edu) is one single utterance and they are linked through 16 different types of relations @xcite . as shown in figure 1 (a), highly related utterances are linked based on discourse relations like question answer pairs, comment and explanation. explicitly modeling these utterances relations in conversations can aid models in recognizing key content for succinct and informative summarization. action graphs are constructed as the "who-doing-what" triplets in conversations which express socially situated identities and activities (gee, 2014). for instance, in figure 1(b) , the action graph provides explicit information between simon, fetch, and tissues for the utterance it is simon who will fetch the tissues, making models less likely to generate summaries with wrong references (e.g., helen will fetch the tissues). to sum up, our contributions are: (1) we pro-pose to utilize discourse relation graphs and action graphs to better encode conversations for conversation summarization. (2) we design structureaware sequence-to-sequence models to combine these structured graphs and generate summaries with the help of a novel multi-granularity decoder. conversation summarization extractive dialogue summarization @xcite has been studied extensively via statistical machine learning methods such as skip-chain crfs @xcite , svm with lda models (wang and cardie, 2013), and multi-sentence compression algorithms @xcite . such methods struggled with generating succinct, fluent, and natural summaries, especially when the key information needs to be aggregated from multiple first-person point-of-view utterances @xcite . abstractive conversation summarization overcomes these issues by designing hierarchical models @xcite , incorporating commonsense knowledge @xcite , or leveraging conversational structures like dialogue acts (goo and chen, 2018), key point sequences @xcite , topic segments @xcite and stage developments @xcite . some recent research has also utilized discourse relations as input features in classifiers to detect important content in conversations @xcite @xcite . however, current models still have not explicitly utilized the dependencies between different utterances, making models hard to leverage long-range dependencies and utilize these salient utterances. moreover, less attention has been paid to identify the actions of different speakers and how they interact with or refer to each other, leading to unfaithful summarization with incorrect references or wrong reasoning @xcite . to fill these gaps, we propose to explicitly model actions within utterances, and relations between utterances in conversations in a structured way, by using discourse relation graphs and action graphs and further combining these through relational graph encoders and multigranularity decoders for abstractive conversation summarization. ## methods to generate abstractive and factual summaries from unstructured conversations, we propose to model structural signals in conversations by first constructing discourse relation graphs and action graphs (section 3.1), and then encoding the graphs together with conversations (section 3.2) as well as incorporating these different levels of information in the decoding stage through a multi-granularity decoder (section 3.3) to summarize given conversations. the overall architecture is shown in figure 2 . ## conclusion in this work, we introduced a structure-aware sequence-to-sequence model for abstractive conversation summarization by incorporating discourse relations between utterances, and the connections between speakers and actions within utterances. experiments and ablation studies on samsum corpus showed the effectiveness of these structured graphs in aiding the task of conversation summarization via both quantitative and qualitative eval-uation metrics. results in zero-shot settings on adcs corpus further demonstrated the generalizability of our structure-aware models. in the future, we plan to extend our current conversation summarization models for various application domains such as emails, debates, and podcasts, and in conversations that might involve longer utterances and more participants in an unsynchronized way.