Challenges in Source Attribution Across Texts

When we read something, it can be helpful to know where the information comes from. For example, knowing the sources of a news article can reveal bias in how the story is told. In historical contexts, understanding the sources helps us see how the author worked and what information they had available. This task of figuring out the sources behind a text is known as Source Attribution.

#Challenges of Source Attribution

Most studies on source attribution focus on scientific papers, where references are commonly cited in a clear format. This makes it easier to find and link sources. However, in areas with less clarity, such as historical texts, it can be tough to identify which source is the right one. Sometimes, multiple editions of a work exist, making it harder to pinpoint a specific reference.

Creating large amounts of fully annotated data for source attribution can be time-consuming and requires specific knowledge. To address this, researchers are looking into different ways to train Models that can find potential sources with less supervision. Early results suggest that semi-supervised methods can perform nearly as well as fully supervised ones, while requiring less effort to annotate.

#Different Types of Information for Source Attribution

There are two main ways authors can indicate their sources in their texts: Text Reuse and citation. Text reuse occurs when an author copies information from their source, which can involve summarizing or rephrasing. This is common in historical writing, where authors often draw from each other's work. Citation, on the other hand, happens when an author explicitly states which source they are using, as seen in scientific articles or on Wikipedia.

Citations can vary in detail. Some may provide just the author and year, while others include the title and page number. Unique identifiers, like URLs or specific headings, can also serve as citations. Each form of citation and text reuse reflects a different relationship between the text and its sources.

#Author vs. Reader Perspective

When thinking about source attribution, it's useful to consider two perspectives: that of the author and that of the reader. From the author's view, the process involves selecting a source and using that information to write their text. This aligns with how models can be designed to help authors retrieve and generate content based on their sources.

From the reader's perspective, the challenge is different. The reader doesn't need to create their text but rather focuses on finding relevant sources to understand a given document better. This leads to a two-stage process where candidate sources are first retrieved and then ranked based on their relevance.

#Models for Source Attribution

To tackle the problem of source attribution, different models are being tested. The first step involves using a basic retrieval model to gather potential sources for a target document. Then, various reranking models refine the list to identify the most relevant sources.

Models can be grouped into different categories based on how they approach source attribution. Some models rely on embedding similarity, while others focus on generative approaches. The ultimate goal is to assess which model performs best and under what conditions.

#Dataset Overview

In this research, two main Datasets are used: one from Wikipedia and another from classical Arabic texts. The Wikipedia dataset consists of a large number of links between articles, while the classical Arabic dataset includes historical writings that often reuse material from various sources. These datasets represent different kinds of relationships between texts and their sources.

The Wikipedia dataset is straightforward, as it involves links to other articles with little modification. In contrast, the classical Arabic texts are more complex, often lacking clear citations or using varying formats. This variety poses unique challenges for source extraction.

#Experiment Setup

The experiments conducted involve comparing several models to understand their effectiveness in source attribution. A baseline model is used as a starting point, and then various reranking models are applied to improve the results. Each model type is designed to test how well it can capture relevant information for the source attribution task.

For the Wikipedia dataset, the goal is to retrieve a section from the cited page using the sentence from the citing page. In the classical Arabic dataset, the aim is to identify the correct source chunk for the given target chunk. Different models are evaluated based on their ability to retrieve and rank potential sources successfully.

#Results of Experiments

The results from the Wikipedia dataset show that a simple retrieval model can achieve a reasonable recall rate. However, when a generative model is introduced, the performance improves significantly. This suggests that incorporating generative capabilities can enhance the ability to find sources effectively.

In the classical Arabic dataset, the baseline model also performs well, but reranking with generative models yields even better results. Interestingly, semi-supervised models provide performance close to fully supervised ones, highlighting that less annotation might still yield valuable outcomes.

#Importance of Fine-Tuning

The findings underscore the importance of fine-tuning models to improve their performance. While generative models can learn complex source relationships, they often require detailed annotations for training. The challenges posed by this requirement could limit their application in broader contexts.

As seen in the experiments, models that lack proper tuning struggle to perform adequately. The results indicate a need for refining approaches to ensure models can effectively learn how to retrieve and rank sources.

#Future Directions

Looking ahead, there are several areas for potential research. For instance, exploring unsupervised methods could prove beneficial, especially with access to improved hardware. Semi-supervised methods deserve further examination, as they can reduce the need for extensive annotation while still achieving good results.

Testing models on larger datasets could validate findings and ensure they translate across various contexts. Additionally, investigating other types of writings, particularly those that sit between the clear citations of Wikipedia and the ambiguity of classical texts, would further enrich research avenues.

The exploration of distinct datasets may also yield new insights. For example, examining the works of historical figures who cited sources in multiple languages could provide valuable data and broaden the understanding of source attribution across cultures.

#Conclusion

The research presents valuable insights into the process of source attribution and the models designed to assist in this task. While current methods demonstrate considerable promise, the field continues to evolve. Future studies will likely yield more refined approaches and innovative techniques, ultimately contributing to better understanding the relationship between texts and their sources.