Multi Document Summarization Evaluation in the Presence of Damaging Content (2023)

Abstract: In the Multi-document summarization (MDS) task, a summary is produced for a given set of documents. A recent line of research introduced the concept of damaging documents, denoting documents that should not be exposed to readers due to various reasons. In the presence of damaging documents, a summarizer is ideally expected to exclude damaging content in its output. Existing metrics evaluate a summary based on aspects such as relevance and consistency with the source documents. We propose to additionally measure the ability of MDS systems to properly handle damaging documents in their input set. To that end, we offer two novel metrics based on lexical similarity and language model likelihood. A set of experiments demonstrates the effectiveness of our metrics in measuring the ability of MDS systems to summarize a set of documents while eliminating damaging content from their summaries.

## introduction in the multi-document summarization (mds) task a summary is generated for a given set of documents. the goal is for the summary to contain the most relevant information within the input documents. mds was demonstrated in various domains including news articles @xcite , wikipedia pages @xcite , and product reviews @xcite @xcite , including either an extractive approach, where the summary consists of spans from the input documents, or an abstractive approach, where the summary is synthetically generated. recent studies @xcite @xcite have defined the concept of damaging documents-documents containing sensitive content that should not be exposed to readers; for example, toxic or offensive content. furthermore, these works have emphasized the challenge of mds in a real-world setting, where imperfect filtering algorithms fail to prevent damaging documents from sneaking into the summarizer's input set. mds systems are, therefore, required to summarize the input documents without exposing any of the damaging content in the output. mds systems are typically evaluated using reference-based metrics by comparing the system summary to one or more reference summaries. a commonly used metric is rouge @xcite , but other metrics (e.g., @xcite @xcite are also being used, some of them do not require a reference summary. the various metrics capture a summary's qualityaspects including relevance, coherence, faithfulness and more @xcite . however, to the best of our knowledge, there is no metric that captures how 'contaminated' the summary is, i.e., the amount of damaging content it contains. the goal of this study is to develop a metric that can appraise the amount of damaging content in a summary. to do so, the notion of 'damaging content' must first be defined. this is challenging since we rely in this work on external annotations, at the document level, which mark damaging documents (e.g. spam reviews). however, a damaging document may contain both damaging and legitimate content, and the legitimate content should be reflected in the summary. therefore, when damaging documents appear in the input, the summarizer's task is to differentiate between legitimate and damaging content, and the evaluation task is to measure its success. consider, for example, the summary presented in figure 1 , generated from some restaurant's negative reviews. while it accurately portrays the negative feedback on food quality and poor service, it also undesirably ex-summary: this place used to be pretty good, but over the past few years it has gone downhill. the food is always undercooked, and the service has gone down hill. the owner is always sitting in the corner drunk, but does not give a shit that he's run this place into the ground. stay away unless you are looking to get drunk and draw far away. figure 1 : a summary generated by the adasum system @xcite for negative restaurant reviews in the d-restaurants dataset @xcite . damaging content is marked in red. another challenge that we face is finding the right balance between measuring the consistency of the summary with the input, and the lack of damaging content. by aggressive filtering, a summarizer can reduce the damaging content in the output, with the cost of less consistency with the input. in contrast, by focusing on consistency, we may preserve a lot of damaging content in the summary, especially in the presence of a lot of damaging content in the input. the metric should take into consideration these two aspects when evaluating the summary quality. in this work we propose two novel metrics that measure the capability of mds systems to reduce the amount of damaging content in their output. the core idea behind our novelty is to add a penalty score denoting the dependency of the summary on damaging documents in the input. the metrics are based on two different approaches: the first extends the rouge metric @xcite to also consider lexical similarity of the summary to the damaging documents. the second extends the pmi metric @xcite to also consider a language model likelihood of the summary, conditioned on the damaging documents. we challenge our evaluation metrics in the domain of customer reviews, where the task is to summarize negative reviews, some of them labeled as spam. experimental results reveal that the suggested metrics can successfully assess the amount of damaging content in a summary. additionally, we experiment in a real world setting where the input data is first filtered by a spam classifier, prior to summarization. in such a setting, choosing a threshold for classification introduces a tradeoff, since with a higher threshold many spam documents will be filtered out, at the cost of filtering legitimate documents as well. our results demonstrate that the proposed metrics are beneficial in setting the optimal threshold for classification. contributions. the main contributions of this work are: • presenting the challenges of evaluating mds systems in the presence of damaging content. the task of multi-document summarization (mds) -generating a summary from a set of source documents -has been researched extensively, with accumulating works that introduce benchmarks and evaluation metrics. traditionally, the typical evaluation protocol estimates a summary's quality by measuring its relevance to the source documents, i.e., how salient the information in the summary is. this is done by comparing the summary with one or more corresponding reference summaries, representing the most salient information within the document sources. some widely used referencebased metrics include rouge @xcite , me-teor @xcite and bertscore @xcite . other metrics attempt to measure summary quality without reference summaries, when they are unavailable (e.g., @xcite @xcite . one family of reference-free metrics uses language models to predict a summary's probability given the source documents @xcite . in addition to relevance, other factors must be taken into consideration in order to assess a summary's quality. early duc foot_0 benchmarks defined readability and content responsiveness criteria @xcite . more recently, @xcite listed the four main quality dimensions as 1) relevance, the salience of summary content with respect to the source, 2) coherence, the linguistic validity of the overall summary structure, 3) fluency, the linguistic quality of individual sentences in the summary, and 4) consistency, the faithfulness of the summary to the source, i.e., whether the summary adheres to the source documents' information. consistency is often measured by comparing the system summary to the source @xcite , and many such metrics have been recently proposed (e.g., @xcite . in our work, we propose an additional evaluation dimension for the cases where certain source documents are damaging. examples of damaging documents include offensive, spam, adult content for children, or classified documents for non-certified users @xcite . while the presence of damaging documents is a known issue in information retrieval tasks @xcite @xcite , this is not the case in summarization tasks. multi-document summarization datasets deliberately prepare document sets that are assumed to be fully legitimate. however, in real-life scenarios, the presence of damaging content cannot be ignored and therefore summarizers are expected not to expose it in their summary. generally, generation of toxic content, a specific kind of damaging content, has recently gained some attention -as posed in the helm framework @xcite . in this work we are interested in the ability of a summarizer to disregard damaging pieces of text in the input. traditional unsupervised summarization methods @xcite , initially treat all parts of the input equally, and may hence fail to omit damaging content. meanwhile, supervised systems @xcite @xcite are faithful to the examples they were trained on, and can therefore generate outputs with certain kinds of content or style. for example, systems may be implicitly trained to output only non-harmful content. other systems use a pipeline method, where certain text spans are filtered out as a first step @xcite . such an approach can attempt to leave out damaging content if desired. our proposed evaluation framework aims to assess a summarizer's ability to remain consistent with allowed source documents and penalizes it for being consistent with forbidden source documents. in §5 we analyze this ability on the three described summarizing approaches. specifically, we demonstrate our evaluation framework in the domain of customer reviews, where the task is to summarize a set of reviews, partly consisting of spam reviews. due to the clear potential utility of customer review summarization, a rich line of research has addressed this task (e.g., @xcite @xcite @xcite , however, to the best of our knowledge, we are the first to view the task in the presence of spam reviews. most of the more recent works on review summarization evaluate on product and service review datasets @xcite . these datasets contain summaries per sets of eight reviews, enabling the use of common summarization evaluation metrics, as described above. while most works focus on evaluating summary relevance, @xcite emphasized the tendency of opinion summarizers to disappoint with quality issues concerning consistency and coherence. we address a more specific type of consistency-checking with our proposed evaluation scheme. ## evaluating mds in the presence of damaging content given a set of documents d, the task of mds is to generate a summary s of d, such that s contains the most salient information in d (relevance), and only information from d (consistency). in this work, we further assume that d can be partitioned into two subsets, unknown to a summarization system. one subset consists of the legitimate documents l, and the other of the damaging documents b, where l ∪ @xmath0. in this setting, a summary should be evaluated for its consistency with l and its avoidance of damaging content in b. as is common in summarization, where consistency is typically measured by approximating the content overlap between the summary and source document(s) @xcite , in our case "consistency" and "avoidance" can be captured through the content overlap between the summary and the two marked subsets of documents. in the rest of this section we present two metrics that exemplify the desired evaluation. the metrics measure a summary's consistency with the legitimate documents, penalizing for its consistency with damaging documents. the first metric is based on rouge @xcite and the second is based on pmi @xcite . ## experimental setup for metric assessment our next step is to assess the proposed metrics for mds in the presence of damaging content. for that, we aim to answer the following research questions: ## conclusion in this work we raise the need to evaluate mds systems for their capability of preventing damaging content in their summaries. we propose two metrics, one based on rouge and the other on the pmi metric. through a set of experiments conducted over customer review datasets and several summarization systems, we demonstrate that our metrics are capable of ranking different summarizers according to how they treat damaging content. importantly, the proposed metrics and the increased awareness to damaging content should motivate further research on optimizing mds in the presence of damaging documents.