Word embeddings play a crucial role in document summarization by capturing the semantic meaning of words and representing them in a numerical format. Document summarization is the task of condensing a longer piece of text into a shorter form, while retaining the key information. Word embeddings provide a foundational representation for understanding and summarizing text.

  One common approach in document summarization is the extractive method, where the summary is generated by selecting and rearranging important sentences from the original document. Word embeddings help in this process by measuring the similarity between sentences or words. By comparing embeddings, algorithms can identify key sentences that capture the essence of the document.

  Word embeddings also enable the generation of contextualized summaries. They can be used to encode both the input document and the generated summary, creating a representation that captures the relationship between them. This allows algorithms to generate summaries that maintain coherence and reflect the content and meaning of the original document.

  Additionally, word embeddings help in addressing the challenge of vocabulary mismatch. Different words and phrases can express similar meanings, but have different surface forms. Word embeddings can capture the semantic similarity between these variations, allowing the summarization model to generalize and recognize different expressions of the same concept.

  In summary, word embeddings are essential in document summarization as they provide a way to represent and analyze the meaning of words and sentences. They enable the selection of important sentences, the generation of contextualized summaries, and help in addressing issues related to vocabulary mismatch. Overall, word embeddings enhance the effectiveness and efficiency of document summarization systems.