Extractiven

Extractiven is a term used in the field of natural language processing (NLP) and information retrieval to describe a type of question-answering system that directly extracts answers from a given text or set of documents. Unlike generative models, which create new sentences to form answers, extractive systems identify and return spans of text that are most relevant to the query. This approach is particularly useful in scenarios where the answer to a question is explicitly stated in the source material, such as in FAQs, technical manuals, or news articles.

Extractive systems typically employ various techniques to identify the most relevant spans of text. These can include keyword matching, semantic similarity measures, and machine learning models trained on annotated datasets. The performance of an extractive system can be evaluated based on metrics such as precision, recall, and F1 score, which measure the accuracy and completeness of the extracted answers.

One of the main advantages of extractive systems is their simplicity and efficiency. They do not require the generation of new text, which can be computationally expensive and prone to errors. Additionally, extractive systems can provide more accurate answers when the source material contains explicit and concise information.

However, extractive systems also have limitations. They may struggle with questions that require inference or synthesis of information from multiple sources. In such cases, generative models or hybrid systems that combine extraction and generation may be more appropriate. Despite these limitations, extractive systems remain a valuable tool in the NLP toolkit, particularly for tasks where the goal is to retrieve specific information from a large corpus of text.