Mitigating Hallucination in Abstractive Summarization with Domain-Conditional Mutual Information (2024)

Abstract: A primary challenge in abstractive summarization is hallucination—the phenomenon where a model generates plausible text that is absent in the source text. We hypothesize that the domain (or topic) of the source text triggers the model to generate text that is highly probable in the domain, neglecting the details of the source text. To alleviate this model bias, we introduce a decoding strategy based on domain-conditional pointwise mutual information. This strategy adjusts the generation probability of each token by comparing it with the token’s marginal probability within the domain of the source text. According to evaluation on the XSUM dataset, our method demonstrates improvement in terms of faithfulness and source relevance.

## introduction abstractive summarization is the task of generating a summary by interpreting and rewriting a source text. state-of-the-art pre-trained language models have achieved remarkable performance in this task @xcite . however, upon closer examination, a common issue emerges: hallucination between the source document and the generated text. prior studies have made efforts to enhance the faithfulness of the summary to the source text, yet hallucination remains a persistent challenge @xcite @xcite . to solve this issue, we introduce a decoding strategy based on domain-conditional pointwise mutual information (pmi dc ). the motivation for pmi dc is that the domain of the source text provokes the model to generate text that is highly probable in the source domain, leading to plausible but factually inconsistent text. building on this motivation, pmi dc method text source ...chairman of the scottish chambers of commerce economic advisory group, said: "our latest economic data shows that many scottish businesses will have a successful 2017... cpmi the scottish chambers of commerce has issued a warning about the outlook for the economy in 2017. pmi dc the scottish chambers of commerce has said it expects the economy to have a "successful" year in 2017. ## preliminaries problem setting we adopt the problem definition in van der @xcite . in abstractive summarization, an input source text, denoted as @xmath0, is condensed into an output string represented by @xmath1, . . . , y t ⟩ ∈ y. this output string is a sequence of tokens from the vocabulary v. each sequence begins with token y 0 and ends with y t , and the length of the output is t + 1. the optimal y that belongs to a valid string set y is obtained via a scoring function as follows: utilizing beam search is a practical solution for searching possible strings. the typical beam search with an autoregressive generation model uses the following scoring function: ## domain-conditional scoring strategy our approach improves upon cpmi by conditioning the probability of a generated token on the source domain. in our domain-conditional strategy (pmi dc ), we employ the following scoring function: to compute the marginal probabilities p(y t |@xmath2, we employ a smaller language model, denoted ## experimental setup dataset we used the extreme summarization dataset, xsum @xcite , which consists of bbc articles as source documents and single-sentence summaries as gold summaries. baselines we examined three baseline decoding methods: standard beam search, pinocchio @xcite . additionally, we analyzed factpeg @xcite , which underwent separate finetuning using factcc and rouge with the source document. models for the summarization model, we utilized encoder-decoder structures of bart @xcite and pegasus @xcite . method text factpeg the crypto-currency, bitcoin. pmi dc the price of the virtual currency bitcoin has fallen sharply in the wake of comments made by one of its most prominent developers. source mike hearn, a zurich-based developer ... published a blog calling bitcoin a "failed" project ... bitcoin's price fell quite sharply over the weekend ... as for the language model, a gpt2-based model @xcite was employed. each of these models was pre-trained on the xsum dataset. more details can be found in appendix b. ## results we presented the results from bart in in ## conclusion we proposed a decoding strategy based on domainconditional pointwise mutual information (pmi dc ) to reduce hallucination in abstractive summarization. pmi dc penalizes the model's tendency to generate text inconsistent with the source document by considering the source text's domain. this simple but innovative approach significantly improves faithfulness and relevance to the source text, as demonstrated through evaluation on the xsum dataset.