282 research outputs found
The Factual Inconsistency Problem in Abstractive Text Summarization: A Survey
Recently, various neural encoder-decoder models pioneered by Seq2Seq
framework have been proposed to achieve the goal of generating more abstractive
summaries by learning to map input text to output text. At a high level, such
neural models can freely generate summaries without any constraint on the words
or phrases used. Moreover, their format is closer to human-edited summaries and
output is more readable and fluent. However, the neural model's abstraction
ability is a double-edged sword. A commonly observed problem with the generated
summaries is the distortion or fabrication of factual information in the
article. This inconsistency between the original text and the summary has
caused various concerns over its applicability, and the previous evaluation
methods of text summarization are not suitable for this issue. In response to
the above problems, the current research direction is predominantly divided
into two categories, one is to design fact-aware evaluation metrics to select
outputs without factual inconsistency errors, and the other is to develop new
summarization systems towards factual consistency. In this survey, we focus on
presenting a comprehensive review of these fact-specific evaluation methods and
text summarization models.Comment: 9 pages, 5 figure
Multi-language transfer learning for low-resource legal case summarization
Analyzing and evaluating legal case reports are labor-intensive tasks for judges and lawyers, who usually base their decisions on report abstracts, legal principles, and commonsense reasoning. Thus, summarizing legal documents is time-consuming and requires excellent human expertise. Moreover, public legal corpora of specific languages are almost unavailable. This paper proposes a transfer learning approach with extractive and abstractive techniques to cope with the lack of labeled legal summarization datasets, namely a low-resource scenario. In particular, we conducted extensive multi- and cross-language experiments. The proposed work outperforms the state-of-the-art results of extractive summarization on the Australian Legal Case Reports dataset and sets a new baseline for abstractive summarization. Finally, syntactic and semantic metrics assessments have been carried out to evaluate the accuracy and the factual consistency of the machine-generated legal summaries
Are NLP Models Good at Tracing Thoughts: An Overview of Narrative Understanding
Narrative understanding involves capturing the author's cognitive processes,
providing insights into their knowledge, intentions, beliefs, and desires.
Although large language models (LLMs) excel in generating grammatically
coherent text, their ability to comprehend the author's thoughts remains
uncertain. This limitation hinders the practical applications of narrative
understanding. In this paper, we conduct a comprehensive survey of narrative
understanding tasks, thoroughly examining their key features, definitions,
taxonomy, associated datasets, training objectives, evaluation metrics, and
limitations. Furthermore, we explore the potential of expanding the
capabilities of modularized LLMs to address novel narrative understanding
tasks. By framing narrative understanding as the retrieval of the author's
imaginative cues that outline the narrative structure, our study introduces a
fresh perspective on enhancing narrative comprehension
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