Detecting Inconsistencies in Narrative Elements of Cross Lingual Nakba Texts (2025)

Abstract: This paper suggests a methodology for contradiction detection in cross lingual texts about the Nakba. We propose a pipeline that includes text translation using Google’s Gemini for context-aware translations, followed by a fact extraction task using either Gemini or the TextRank algorithm. We then apply Natural Language Inference (NLI) by using models trained for this task, such as XLM-RoBERTa and BART to detect contradictions from different texts about the Nakba. We also describe how the performance of such NLI models is affected by the complexity of some sentences as well as the unique syntactic and semantic characteristics of the Arabic language. Additionally, we introduce a method using cosine similarity of vector embeddings of facts for identifying missing or underrepresented topics among historical narrative texts. The approach we propose in this paper provides insights into biases, contradictions, and gaps in narratives surrounding the Nakba, offering a deeper understanding of historical perspectives.

## introduction nakba (arabic for catastrophe) refers to the displacement of hundreds of thousands of palestinians from palestine during the 1948 war between arabs and israel (united nations, 2024). as a result of this catastrophe, approximately 750,000 palestinians were forcefully displaced, 15,000 killed, and more than 531 towns and villages @xcite . with this catastrophe happening 76 years ago, the narratives that we have now surrounding it vary, with belief, denial, or skepticism. conflicting narratives regarding the history of the nakba preceded it, and eventually led to it. a clear example that displays how a conflict in narratives contributed to the events leading up to nakba is the famous phrase "a land without a people for a people without a land". this phrase, believed by many historical references to be a "zionist slogan" @xcite , was in stark contrast to the 690,000 people who lived in palestine in 1914 (palestinian central bureau of statistics (pcbs), 2021) before the balfour declaration and the waves of immigration to palestine that followed. by considering such examples of how a narrative can influence and be influenced by remarkable events of history, we establish the need for a systematic approach to review such narratives, and point to pieces of historical evidence that have been tampered with, dropped or manipulated. in this paper, we propose a method that incorporates the use of natural language processing (nlp) to review pieces of written text, compare texts from contrasting backgrounds, and lastly presents sentences where this contrast in facts happen, which in return indicates contradiction of information between the subject sources. the system also presents what statements have been left out in one source but mentioned in another. this highlighting of the contradictions found in the subject text, as well as some text failing to mention certain facts, can lead to better detect biases. by flagging such contradictions, historians and experts on the nakba can make informed remedial decisions based on the approach outlined in this paper. the system we propose begins by taking two input texts, the focus languages here are english and arabic, and the texts are expected to reflect differing historical narratives about the nakba. since arabic-speaking historians are the target, texts in english are first translated to arabic. in a previous work of ours @xcite , we reached the conclusion that using gemini for translation performed better than using machine translation models such as marianmt. therefore, we propose using gemini to perform this step. then we extract facts that represent the ideas of the texts. for this we suggest two methods of providing summaries, once by prompting gemini, and the other by using the textrank algorithm. the facts extracted by this step are assumed to reflect the contradictions and overlooked facts between the texts. lastly, we perform contradiction detection by utilizing the labels provided by nli models such as xlm-roberta (xlm-roberta-large-xnli) and bart (bart-large-mnli). this gives a score for the relationship between a pair of sentences, to indicate either an entailment, neutral, or contradiciton relationship. for finding gaps in fact representation over the texts, we propose using embedding techniques on the facts extracted from the texts, then use similarity metrics, such as cosine similarity, to find unique sentences that are missing in the corresponding texts. ## background in this paper, we suggest the use of automatic summarization of the subject texts. automatic text summarization extracts the main ideas of a text document @xcite . summarization can happen in two types, extractive and abstractive summaries. an extractive summary produces a summary based on the sentences used in the text itself rather than producing a unique summary. it looks for identical information in documents and assigns a score to each statement based on how well it explains the other facts of the document. an abstractive summary, in contrast, generates a unique summary by rephrasing and restructuring the most important information from the original text @xcite . however, summarization is a complex nlp problem influenced by text type, length, vocabulary level, and named entities. however, due to its abstract nature, it remains a longstanding issue @xcite . google's gemini is designed to improve automated text summarization by mimicking humanlike sentence-level styles. it combines extractive and abstractive techniques, allowing for fine control over summary style and quality, which reduces risks like factual inaccuracies in summaries. gemini introduces a "fusion index" to analyze and adjust sentence styles within new datasets, enhancing flexibility for different applications @xcite . while effective, this approach still lacks a clear way to measure its ability to generate fully abstracted summaries, marking an area for further exploration @xcite . textrank is an unsupervised extractive text summarization algorithm that ranks sentences by extracting the main ideas from a document depending on their importance. it is related to google's pagerank, which ranks web pages for online search results @xcite . with that said, it should be noted that research points to limitations in arabic summarization techniques. this is linked to many reasons including the scarcity of annotated datasets in arabic, the complexity of arabic linguistics, morphology, and syntax. all these reasons lead to difficulties in obtaining a coherent summary that does not change the intended tone and meaning of the original text @xcite a notable technique in relationship inference from texts is the natural language inference (nli) task. nli classifies the relationship between a pair of premise and hypothesis as either entailment, contradiction, or neutral. it forms the basis for higher-level nlp tasks like question-answering and summarization @xcite . in arabic however, nli is a challenging task because of the lexical ambiguity of the language, lack of large entailment datasets, etc. (jallad and ghneim, 2022) ## conclusion in this paper we proposed a method of contradiction detection in historical texts about the nakba, and the sensitivity of dealing with the different narratives surrounding the issue. we proposed the use of google's gemini to provide contextaware translations of texts in english, as the audience this work is directed towards is arabicspeaking historians and experts. in addition, we also compared prompting gemini to provide facts summarized from textual content, in addition to using the textrank algorithm for the same purpose. the core of the paper then employs nli models such as xlm-roberta (xlm-roberta-large-xnli) and bart (bart-large-mnli) to detect contradictions in pairs of statements taken from different texts about the nakba. the findings suggest that the performance of these models on this specific task is influenced by the complexity of the sentences and the arabic linguistic features in general. another important part of this paper is a suggested method of finding sentences or topics that are not mentioned in a specific text with a specific narrative. our aim was that the methodologies suggested in this paper enable an expert in history to gain deeper analysis of biases, contradictions, and gaps in historical narratives from both sides of history. unique gemini facts with translations from @xcite ‫ﺍ‬ ‫ﺍ‬ ‫ﺍ‬ ‫ﺍ‬ ُ ‫ﺩ‬ ‫ﺍ‬ ‫ﻥ‬ ‫ﻭ‬ ‫ﺯ‬ ‫ﺩﻭﺝ:‬ ‫ﻥ‬ ‫ﺍ‬ ‫ﻥ‬ ‫ﻭ‬ ‫ﺯ‬ ‫ﻭ‬ ‫ﻥ،‬ ‫ﺭﺝ‬ ‫ﻥ.‬ ‫ﺍ‬ "zionist identity was built from the beginning on a two-fold negation: it negates time and space of the jews outside zion, a 'negation of exile' which extends beyond the realm of religion, and it negates time and space of those indigenous to the territory of zion" (bronstein, 2009) ‫ﺍﺙ‬ ‫ﺍ‬ ‫ﻭ‬ ، ً ‫ﻭ‬ 1948 ‫ﻡ‬ ‫ﺍ‬ ‫ﺍ‬ ‫ﺍ‬ ‫ﺭﺩ‬ ‫ـ‬ ‫ﻭ‬ ، ‫ﺍ‬ ‫ﺕ‬ ‫ﺍ‬ ‫ﺍ‬ ‫ﺀ‬ ‫ﺍ‬ ‫ﺍ‬ ‫ﻥ'‬ ‫ﺍ‬ ‫ﻥ‬ ‫'ﺍ‬ . ‫ﺩ‬ ‫ﺍ‬ ‫ﻭ‬ ‫ﺍ‬ ، ‫ﺍ‬ ‫ﻥ‬ ‫ﺍ‬ "according to zionism, the violent events around 1948 did in fact occur, but only in form of an unavoidable response to the disturbance caused by the 'locals,' who did not accept the establishment of the new entity, the jewish state." (bronstein, 2009) ‫ﻭﺭﻱ،‬ ‫ﺙ‬ ‫ﺍ‬ ‫ﻭ‬ ‫ﺫﺍ‬ . ‫ﺍ‬ ‫ﺍ‬ "on the other hand, and paradoxically, the nakba -the violent expulsion of the inhabitants of the country and the transformation of those remaining into refugees in their homeland, or into secondclass citizens -is a necessary event, because it brought about the realization of the ethnically pure, closed and autonomous zionist subject which builds itself in the framework of a state aimed exclusively for him/her." (bronstein, 2009) ‫ﺀ‬ ‫ﺭﺅ‬ ‫ﺃ‬ ، ، ‫ﺍ‬ ‫ﺫ‬ ‫ﺩﻱ‬ ‫ﺍ‬ ‫ﺍ‬ ‫ﻭﻕ‬ ‫ﺍ‬ ، ‫ﺩ‬ ‫ﺯﺭ‬ ‫ﺩ‬ ‫ﺍ‬ ‫ﺓ‬ ‫ﺍﺕ‬ ‫ﺩ‬ ‫ﺍ‬ . ‫ﺩ‬ ‫ﻉ‬