What are the limitations of extractive summarization?

What are the limitations of extractive summarization?

Extractive summarization has several limitations, including:

1. Lack of coherence: Extracted sentences may not flow well together.
2. Context loss: Important context can be lost when only selecting sentences.
3. Redundancy: Similar sentences may be chosen, leading to repetitive summaries.
4. Limited understanding: It doesn't grasp the overall meaning or intent of the text.

Can you give an example of a situation where it fails?

Sure! Imagine summarizing a news article about a complex political event. Extractive summarization might pick sentences that mention key figures but miss the underlying causes or implications, resulting in a summary that lacks depth and understanding.

How does it compare to abstractive summarization?

Abstractive summarization generates new sentences that capture the essence of the text, allowing for better coherence and context retention. In contrast, extractive summarization simply selects existing sentences, which can lead to disjointed summaries.

Are there any advancements in extractive summarization?

Yes, recent advancements include using machine learning techniques to improve sentence selection and coherence, as well as integrating contextual understanding to enhance the quality of the summaries.