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1. First, import the necessary modules:

from nltk.corpus import reuters
from nltk import sent_tokenize
import pandas as pd

1. Load the Reuters Corpus:

sentences = reuters.sents()

1. Use sent_tokenize to create a list of sentences:

sentences = [sent_tokenize(" ".join(sentence)) for sentence in sentences]

1. Create a DataFrame from the sentences list:

df = pd.DataFrame({'text': [item for sublist in sentences for item in sublist]})

1. Use corpus_segment to add a column of the corresponding categories for each sentence:

df['category'] = reuters.categories(fileids=[idx for idx, _ in reuters.fileids() if _ in df['text'].tolist()])

This will result in a DataFrame with two columns: text containing the sentences and category containing the corresponding categories.