What is Your Article Based On? Inferring Fine-grained Provenance (2021)

Abstract: When evaluating an article and the claims it makes, a critical reader must be able to assess where the information presented comes from, and whether the various claims are mutually consistent and support the conclusion. This motivates the study of claim provenance , which seeks to trace and explain the origins of claims. In this paper, we introduce new techniques to model and reason about the provenance of multiple interacting claims, including how to capture fine-grained information about the context. Our solution hinges on first identifying the sentences that potentially contain important external information. We then develop a query generator with our novel rank-aware cross attention mechanism, which aims at generating metadata for the source article, based on the context and the signals collected from a search engine. This establishes relevant search queries, and it allows us to obtain source article candidates for each identified sentence and propose an ILP based algorithm to infer the best sources. We experiment with a newly created evaluation dataset, Politi-Prov, based on fact-checking articles from www.politifact.com ; our experimental results show that our solution leads to a significant improvement over baselines.

## introduction misinformation is on the rise, and people are fighting it with fact checking. however, most of the work in the current literature @xcite @xcite focuses on automating factchecking for a single claim. in reality, a claim can be complex, and proposed as a conclusion of an article. therefore, understanding what information supports the article, especially information that was not originated within the same article, and where it originates from, are very important for readers who want to determine whether they can believe the claim. figure 1 shows an example of such a claim, "marco rubio says anthony fauci lies about masks. fauci didn't." 2 with its article from politifact.com. a critical reader of the content will find that several major sources support the author's claim: source article 1 in the figure is cbs news,"60 minutes" interview with anthony fauci, on march 8, 2020, which reveals that dr. fauci's main point was to preserve masks for those who were already ill and people providing care. if readers can validate all sources used in the article, they will be able to determine whether the article is trustworthy. in this paper, our goal is to automatically find these sources for a given article. this is a different problem from fact-checking: factchecking seeks evidence for a claim, while here we only care about the information sources the authors used when they were writing. furthermore, the problem we address is critical also to authors who want to give credit to those who have contributed to their article, and it enables a recursive analysis that can trace back to the starting points of an article. this motivates the study of provenance for natural language claims, which describes where a specific claim may have come from and how it has spread. early work @xcite proposed a formulation to model, and a solution to infer, the provenance graph for the given claim. however, that model is insufficient to capture the provenance of an article, because (1) an article consists of multiple claims, and it leverages information from other sources, therefore the provenance of all claims should be included in the article's provenance; (2) the inference solution they proposed can only extract domain-level provenance information, e.g., cbsnews.com, while it can not directly link the claim to its source article, e.g., https://www.cbsnews.com/news/preventingcoronavirus-facemask-60-minutes-2020-03-08/. such fine-grained provenance information is important because it can help people understand the original context that influenced the information they read. therefore, in this work, we argue that the notion of a provenance graph should be extended to incorporate provenance for articles, and that we need a more comprehensive solution that can identify important external information used in the article and infer its corresponding source article: namely, its fine-grained provenance information. technically, capturing fine-grained provenance for an article is challenging because (1) there may be large numbers of sentences in an article, and not all are from external sources nor important (thus, their provenance may not be worth considering); the key contributions of this paper are (1) we introduce and formalize the problem of inferring finegrained provenance for an article; (2) we propose a general framework to infer the source articles that have provided important information for the given article, including (a) a ranking module that can identify sentences that contain important external information based on the main topic and the main entities in the article; (b) a query generator that can generate possible metadata for the source article, e.g., the title, the published date, the source website, based on the context of the selected sentences; (c) an integer linear program (ilp) based algorithm to jointly identify the source articles from all of the candidates. (3) to evaluate our solutions, we collect a new dataset politi-prov from politifact.com, and our experimental results show that the solution we proposed can lead to a significant improvement compared with baselines. given an article d, we are to capture its finegrained provenance, by inferring k source articles sa k (d) that provide the most important information for d. we adopt the notion of provenance from @xcite , while in this paper, we focus on inferring provenance for a claim based on the information from the given article. to find sa k (d), there are three subproblems we need to solve. ## problem statement first, we need to locate the important external information in d, which means we need a sentence ranking module that can estimate a score σ i for each sentence in @xmath0, based on how likely s i contains external information. then we will choose top-k sentences based on their score, and try to find source articles for those sentences. second, for each selected sentence, we need to generate a list of candidate links, which can be its source articles. to achieve this goal, we take advantage of a search engine, based on which we can access all of the articles on the web. as we have discussed in section 1, directly searching the identified sentence on a search engine may result in a low precision of finding the correct source article. therefore, we propose to develop a query generator to generate the possible metadata of the target source article as new search keywords, so that the search engine is more likely to recall source articles. we then collect all of the search results as the candidates for a selected sentence. finally, we need to infer the correct source article from the candidates, for each identified sentence. figure 2 depicts the three steps we need to conduct to infer the fine-grained provenance, which correspond to the three subproblems listed above. we will elaborate the details of each step in section 4. ## politi-prov dataset to the best of our knowledge, there is no existing dataset that can support inferring fine-grained provenance for an article, therefore we create a new dataset based on the fact-checks from politifact .com to support the training and the evaluation of this problem. specifically, we crawled all of the fact-check questions from politifact.com on 4 different issues: coronavirus, health care, immigration, taxes in september, 2020. for each question, we further crawled its webpage to obtain (1) the title, which is actually the fact-check question itself, (2) the sections of the main text and (3) the "our sources" section listing all of the articles (including urls) that provide important information mentioned in the fact-check article. figure 3 shows an example of such a section. figure 3 : an example of "our sources" section of an article from politifact.com, where we obtain the gold fine-grained provenance of the article. furthermore, we extract all of the hyperlinks in the webpage, which can tell us where the source articles are mentioned in the main text. to sum up, we use the main text of each webpage as the given article, and the source articles listed in the section of "our sources" as the ground truth our system wants to return. we want to note it is possible that there may be some sources missing in the ground truth we can obtain, therefore, we focus more on the recall in the evaluation. overall, we collected data from 1765 articles, where we use 883 of them for training, and 441 and 441 for validation and testing respectively. on average, each article has 9.8 source articles. ## inferring fine-grained provenance in this section, we will elaborate how we solve the problems proposed in section 2. ## experimental evaluation in this section we aim to answer the following research questions: rq1 can we correctly identify the sentences that refer to important external information in the given article? rq2 given the identified sentences, can we generate the metadata of the target articles from the context? rq3 given a list of candidates for each identified sentence in the article, can we assign the correct candidate to each identified sentence? ## related work our work builds on earlier work on claim provenance (see section 2 for a discussion). beyond that, we discuss below additional related work. fact-checking fact-checking is related to our problem, since there is usually a document retrieval step to find articles that may provide evidence in most of the solutions @xcite @xcite . typically, the input of fact-checking is a single claim instead of an article, therefore it is hard to directly extend their solutions to our problem. even though fact-checking may find various evidentiary articles for the claim, the source articles we are looking for are those that have been used by the author, which is actually a specific subset of the articles that fact-checking targets to, and the size is also much smaller. furthermore, we try to extract the metadata of the source articles from the text to support a better search, which is not considered in the document retrieval step of fact-checking. recommending citations recommending citations for scholarly articles has similarities to our work. the source articles we are looking for can be considered as the citations of the given news article that should be recommended. however, the meaning of the "reference" is different in these two problems. when recommending citations for a paper, the system is to look for previous works that are related to the arguments in the given paper. the argument was created by the author, and the criteria of the recommendation is the relatedness. while inferring provenance is to do reverse engineering to the given article, so that we can find the articles whose information or claims were actually used when the author was writing. technically, there are two types of citation recommendation systems @xcite . one is called local @xcite , that is, a system takes a few sentences (and an optional placeholder for the candidate citation) as input and recommends citations based on the context of the input sentences. another one is called global @xcite @xcite , that is, a system takes the entire article (and its meta-data which is optional) as input and recommends cita-tions for the paper. our solution is more related to local recommendation systems, while we do not assume we can access all of the articles that can be cited and have a way to represent them to be vectors. therefore, we propose to learn a query generator, which is different with previous works. furthermore, we do joint inference for all of the identified sentences in the article, which is actually a global inference. ## conclusion and future work we propose new techniques to infer fine-grained provenance for an article that contains multiple claims; this is important for a critical reader to understand what information supports the article he/she is reading and what its origins are. the inference consists of models that can identify the sentences that refer to important external information, generate the metadata that can make it more likely to recall the source articles using a search engine, and do an ilp inference to jointly determine the correct source articles from the candidates. we create a new dataset, politi-prov, for this task, and our evaluation on it demonstrates the effectiveness of each component, and shows a big improvement compared with the baselines of finding source articles. however, the problem has not been solved yet. as shown in the analysis, a better text generation model would further improve the performance. furthermore, it has also been revealed in the experiments that the gold metadata can only recall only around 40% of the source articles, which actually becomes a bottleneck. therefore, it would be an interesting future work direction to explore what other information should be added to the query, besides the target metadata, so that we can recall more source articles.