RTSUM : Relation Triple-based Interpretable Summarization with Multi-level Salience Visualization (2024)

Abstract: In this paper, we present RTSum, an unsupervised summarization framework that utilizes relation triples as the basic unit for summarization. Given an input document, RTSum first selects salient relation triples via multi-level salience scoring and then generates a concise summary from the selected relation triples by using a text-to-text language model. On the basis of RTSum, we also develop a web demo for an interpretable summarizing tool, providing fine-grained interpretations with the output summary. With support for customization options, our tool visualizes the salience for textual units at three distinct levels: sentences, relation triples, and phrases. The code, demo, and video are publicly available.

## introduction text summarization has emerged as a critical tool in the era of information overload, enabling users to quickly understand the essence of long text. among various summarization techniques, abstractive summarization has gained significant attention due to its ability to generate fluent and concise summaries that capture the main ideas of the source text @xcite @xcite @xcite . nevertheless, despite their advantages in flexibility and reduced redundancy compared to extractive methods, abstractive methods inherently lack interpretability. that is, the absence of a direct link to the source text can make it difficult to trace back the source of information, which makes the summary lack interpretability. interpretability in summarization is important to provide users a way to cross-check that the generated summary is factually consistent, and to provide more context to dive into if one wants to know more about the summarized content. to generate an interpretable summary, extractive summarization techniques can offer advantages. as they directly extract sentences from the text, the sentences themselves serve as the source of information @xcite . however, a significant drawback of many extractive methods lies in their sentence-level operation, which limits their ability to extract fine-grained key information @xcite . in many cases, a single sentence describes multiple diverse pieces of information that should be treated as distinct facts for summarization. by selecting entire sentences, these methods may include unnecessary or redundant information in the summary, reducing both its efficiency and readability. to enhance the interpretability of the summarization process by incorporating fine-grained key information, our focus lies on leveraging relation triples as the basic unit for summarization. a relation triple in the form of (subject, predicate, object) concisely describes a single piece of information corresponding to its relation (i.e., predicate) between two entities (i.e., subject and object), and it can be effectively identified from a source document by using open information extraction (ope-nie) systems @xcite . using relation triples, our main idea embodies selection-and-sentencification, which achieves a combination of extractive and abstractive summarization methods. specifically, we first select only a few relation triples according to their importancesalience -within the document for summarization, and then reassemble the selected relation triples into the final output summary. this two-step approach enhances the interpretability of the summarization by providing clear explanations for the salience scores of relation triples and their contributions to the final summary. this clarity allows users to understand the crucial elements driving the summarization process effectively. formally, we present an unsupervised relation triple-based summarization framework, named rtsum. for relation triple selection, rtsum identifies heterogeneous textual information units with various granularity, which are (1) sentences, (2) relation triples, and (3) phrases, to utilize their own salience all together. under the principle that more salient textual units are much more relevant to other units semantically and lexically, it models the multi-level salience from the three distinct textual units. then, it selects the k most salient relation triples based on the multi-level salience scores. for relation triple sentencification, rtsum employs a neural text-to-text architecture as a relation combiner to transform the relation triples into the summary sentences. the relation combiner is effectively optimized in a self-supervised manner by using source sentences (sampled from training documents) and their relation triples (extracted from the sampled sentences) as targets and inputs, respectively, while not requiring any reference summaries of its training documents. building upon the rtsum framework, we develop an online demo to showcase an interpretable text summarization tool. given an input document, our tool generates a concise summary, while simultaneously offering fine-grained interpretations by visually depicting the multi-level salience of textual units within the source document. for clarity in visualization, the tool highlights text spans (i.e., textual units) based on their salience score, with numerical ranks provided as annotations. furthermore, our tool offers customization options, allowing users to personalize the visualization according to their preferences and specific purposes. our multi-level salience visualization empowers users to easily identify the textual units that mostly influence the final summary; it also provides valuable insights into the salient semantic structure of the document at a glance, enhancing users' overall understanding of the summarization process. ## preliminary textual information units. we utilize three different types of textual information units with various granularity: sentences, relation triples, foot_0 and phrases. all sentences and relations are extracted from a source document by using open information extraction (openie) systems. among several implementations, we employ openie 5 foot_1 @xcite released by uw and iit delhi. similarly, all noun and verb phrases in the document are identified based on pos labels tagged by the spacy library. ## rtsum: relation triple-based summarization framework our summarization framework, named rtsum, consists of the two steps: (1) information selection for identifying the salient relation triples based on multi-level salience from various textual information units (section 3.1), and (2) information sentencification for combining the selected relation triples into plausible sentences with the help of a neural text generator (section 3.2). figure 1 illustrates the overall process of our rtsum framework. ## demo: interpretable summarizing tool based upon our rtsum framework, we build an interpretable summarizing tool that provides not only the final summary of an input document but also its fine-grained interpretations. ## conclusion in this paper, we introduce a summarization framework, called rtsum, which leverages relation triples as the basic units for summarization. building upon this framework, we have developed a web demo for an interpretable summarizing tool that effectively visualizes the salience of textual units at three distinct levels. through our multi-level salience visualization, users can easily identify textual units impacting the summary and gain insights into the document's salient semantic structure. our rtsum framework and its user-friendly tool can effectively capture the essence of a document while maintaining interpretability. the fusion of extractive and abstractive approaches, coupled with intuitive multi-level visualization, holds promise for applications requiring succinct, accurate, and interpretable summaries.