Unsupervised Multi-Granularity Summarization (2022)

Abstract: Text summarization is a user-preference based task, i.e., for one document, users often have different priorities for the summary. As a key aspect of customization in summarization, granularity is used to measure the semantic coverage between the summary and source document. However, developing systems that can generate summaries with customizable semantic coverage is still an under-explored topic. In this paper, we propose the first unsupervised multi-granularity summarization framework, GranuSum. We take events as the basic semantic units of the source documents and propose to rank these events by their salience. We also develop a model to summarize input documents with given events as anchors and hints. By inputting different numbers of events, GranuSum is capable of producing multi-granular summaries in an unsupervised manner. Meanwhile, we annotate a new benchmark GranuDUC that contains multiple summaries at different granularities for each document cluster. Experimental results confirm the substantial superiority of GranuSum on multi-granularity summarization over strong baselines. Furthermore, by exploiting the event information, GranuSum also exhibits state-of-the-art performance under the conventional unsupervised abstractive setting.

## introduction text summarization aims to condense and summarize long documents into a concise paragraph containing the essential points of the original texts @xcite @xcite @xcite . notably, the requirements for summarization are highly customized and personalized for different users @xcite @xcite . therefore, generating quality summaries to meet granularity, a key aspect of customization in summarization, is used to measure the degree of semantic coverage between summary and source documents @xcite . to cater to the diverse needs of readers, the granularity level of summaries often varies in a wide range. as shown in most existing summarization models and benchmarks focus solely on single-granularity summarization. it limits the ability of these systems to adapt to different user preferences and generalize to a wider range of granularity scenarios. to alleviate this issue, some recent studies are dedicated to controlling the length of summary @xcite @xcite . however, as a surface-level feature of the summary, longer length does not equate to a higher degree of semantic coverage. in other words, the length limit can be easily satisfied by talking less/more details about the same event, but this is in contrast with the concept of summarization. another research direction is query/aspect-based @xcite @xcite and interactive summarization @xcite . based on different queries, models can focus on different parts of the document and create summaries of various granularities. in practice, it requires a user to provide a query, implying that the user must have prior knowledge of the topic of the source text. therefore, automatic granularity-aware summarization model is still an under-explored topic. in this paper, we propose an unsupervised multi-granularity summarization framework called granusum. unlike previous work based on supervised learning to provide guidance signals, such as salient sentences @xcite , keywords @xcite , and retrieved summaries @xcite , our approach does not rely on any manually labeled data. to measure the granularity, we first regard events as the basic semantic units of the input texts because events carry rich semantic information and are considered as informative representations in many nlp tasks @xcite @xcite . overall, our system consists of two event-related components: event-aware summarizer and event selector. specifically, given the document and randomly selected events in it as hints, we pre-train an abstractive summarizer that can recover eventrelated passages. furthermore, in an unsupervised manner, our event selector selects the events with high salience from the original text by candidate events pruning and ranking. finally, through selecting different numbers of anchor events based on event selector, we can control the summarizer to generate summaries containing different events, thus covering different numbers of semantic units of the original text. with our proposed approach, granusum becomes an unsupervised framework for multi-granularity summary generation. to evaluate the multi-granularity summarization systems, we re-annotate duc2004 @xcite as the first benchmark in this direction (denoted as granuduc). given multiple documents on the same topic, we annotate summaries at three levels of granularity with different semantic coverage. also, to utilize the existing datasets for a supplement evaluation, we propose to divide several large-scale summarization datasets into buckets with summaries at different granularity levels to further evaluate the model performance. experimentally, granusum surpasses strong summarization systems on all the multi-granularity evaluations. additionally, we conduct conventional unsupervised abstractive summarization experiments on three typical benchmarks in different domains. results demonstrate that granusum also substantially improves the previous state-of-the-art model under the traditional setting. ## multi-granularity framework in this section, we first describe in detail our framework granusum, which has two major compo-nents: event-aware summarizer and event selector. combining them enables multi-granularity generation. the overall framework can be seen in figure 1 . then, we introduce the new humanannotated benchmark, granuduc, which can be used for multi-granularity evaluation. ## experiments we design three settings of experiments: 1) experiments on granuduc, 2) bucket-based evaluation, 3) unsupervised abstractive summarization. the first two settings constitute a new testbed for multi-granularity summarization, where bucket means that we divide the existing dataset into different buckets according to semantic coverage to make the evaluation more comprehensive. in addition to this scenario, the last experiment auxiliarily evaluates the quality of summaries generated by our framework under the conventional unsupervised abstractive summarization setting. ## conclusion in this paper, we highlight the importance of multigranularity summarization systems in catering to user preferences and applying them to real-world scenarios. to facilitate research in this direction, we propose the first unsupervised multi-granularity summarization framework granusum and build a well-established testbed. experiments demonstrate the effectiveness of our framework.