  Text classification is essential for organizing unstructured text.
Traditional methods rely on human annotations or, more recently, a set of class
seed words for supervision, which can be costly, particularly for specialized
or emerging domains. To address this, using class surface names alone as
extremely weak supervision has been proposed. However, existing approaches
treat different levels of text granularity (documents, sentences, or words)
independently, disregarding inter-granularity class disagreements and the
context identifiable exclusively through joint extraction. In order to tackle
these issues, we introduce MEGClass, an extremely weakly-supervised text
classification method that leverages Mutually-Enhancing Text Granularities.
MEGClass utilizes coarse- and fine-grained context signals obtained by jointly
considering a document's most class-indicative words and sentences. This
approach enables the learning of a contextualized document representation that
captures the most discriminative class indicators. By preserving the
heterogeneity of potential classes, MEGClass can select the most informative
class-indicative documents as iterative feedback to enhance the initial
word-based class representations and ultimately fine-tune a pre-trained text
classifier. Extensive experiments on seven benchmark datasets demonstrate that
MEGClass outperforms other weakly and extremely weakly supervised methods.
