The goal of multi-document text summarizing is to automatically generate a succinct yet thorough summary from
multiple connected sources. The growing volume of digital text data is making the task of document summarization more
challenging. More effective summarization is now possible because to significant advancements in artificial intelligence
techniques and pre-trained language models; nonetheless, the majority of deep learning approaches require a significant amount
of processing power and large training datasets. This work suggests a multi-document text summarizing method powered by
artificial intelligence that makes use of supervised learning and sentence feature modeling.
The proposed system begins with heavy preprocessing that involves lemmatization, normalization, stop-word removal, and
sentence segmentation. Statistical and structural features, including the TF–IDF representation, length of the sentence,
normalized position of the sentence, and numeric token count, are extracted to denote sentence significance. Like other
supervised extraction algorithms discussed in previous literature, a Logistic Regression classifier is created using a balanced
dataset to classify sentences into significant or insignificant categories. Probabilities are used for ranking of selected sentences,
while cosine similarity is applied under a certain word limit constraint to avoidredundancies.
The suggested approach demonstrates effective accuracy while needing very little processing effort, according to experimental
examination of the DUC 2003 corpus. Based on the ROUGE measure, the model produces ROUGE-1, ROUGE-2, and
ROUGE-L scores of 0.1722, 0.0306, and 0.1048, respectively. It demonstrates how basic AI models combined with useful
sentence-based features could be a reliable benchmark for information extraction from several publications. To increase the
effectiveness of summaries, the authors intend to look into contextual representation algorithms like Sentence-BERT and
implement human evaluation standards in subsequent studies.
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