Update the description for the NSL pip package.

The current description is a copy of the README.md file. This contains relative links to markdown files which don't work on the py-pip website. Further, there is a lot of information which really doesn't belong here.

PiperOrigin-RevId: 267624409

Author: arjung

setup.py

@@ -11,12 +11,11 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-"""Setup for TensorFlow Neural Structured Learning package."""
+"""Setup file for the TensorFlow Neural Structured Learning pip package."""
 
-import setuptools
+import textwrap
 
-with open("README.md", "r") as fh:
-  long_description = fh.read()
+import setuptools
 
 INSTALL_REQUIRES = [
     "absl-py",
@@ -25,13 +24,49 @@
     "six",
 ]
 
+NSL_OVERVIEW = textwrap.dedent("""\
+    Neural Structured Learning (NSL) is a new learning paradigm to train neural
+    networks by leveraging structured signals in addition to feature inputs.
+    Structure can be explicit as represented by a graph or implicit as induced
+    by adversarial perturbation.
+
+    Structured signals are commonly used to represent relations or similarity
+    among samples that may be labeled or unlabeled. Leveraging these signals
+    during neural network training harnesses both labeled and unlabeled data,
+    which can improve model accuracy, particularly when the amount of labeled
+    data is relatively small. Additionally, models trained with samples that are
+    generated by adversarial perturbation have been shown to be robust against
+    malicious attacks, which are designed to mislead a model's prediction or
+    classification.
+
+    NSL generalizes to Neural Graph Learning as well as to Adversarial Learning.
+    The NSL framework in TensorFlow provides the following easy-to-use APIs and
+    tools for developers to train models with structured signals:
+
+    * Keras APIs to enable training with graphs (explicit structure) and
+      adversarial pertubations (implicit structure).
+
+    * TF ops and functions to enable training with structure when using
+      lower-level TensorFlow APIs.
+
+    * Tools to build graphs and construct graph inputs for training.
+
+    The NSL framework is designed to be flexible and can be used to train any
+    kind of neural network. For example, feed-forward, convolution, and
+    recurrent neural networks can all be trained using the NSL framework. In
+    addition to supervised and semi-supervised learning (a low amount of
+    supervision), NSL can in theory be generalized to unsupervised learning.
+    Incorporating structured signals is done only during training, so the
+    performance of the serving/inference workflow remains unchanged.""")
+
 setuptools.setup(
     name="neural-structured-learning",
     version="1.0.0",
     author="Google LLC",
-    description="TensorFlow Neural Structured Learning",
-    long_description=long_description,
-    long_description_content_type="text/markdown",
+    description="Neural Structured Learning is an open-source TensorFlow "
+    "framework to train neural networks with structured signals",
+    long_description=NSL_OVERVIEW,
+    long_description_content_type="text/plain",
     url="https://github.com/tensorflow/neural-structured-learning",
     packages=setuptools.find_packages(),
     install_requires=INSTALL_REQUIRES,